Data Is Everybody’s Business

Management on the Cutting Edge series

Abbie Lundberg, series editor

Published in cooperation with MIT Sloan Management Review

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Winning the Right Game: How to Disrupt, Defend, and Deliver in a

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Work without Jobs: How to Reboot Your Organization’s Work Operating

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The Future of Competitive Strategy: Unleashing the Power of Data and

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Data Is Everybody’s Business: The Fundamentals of Data Monetization

Barbara H. Wixom, Cynthia M. Beath, and Leslie Owens

Data Is Everybody’s Business

The Fundamentals of Data Monetization

Barbara H. Wixom, Cynthia M. Beath, and Leslie Owens

The MIT Press

Cambridge, Massachusetts

London, England

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The MIT Press would like to thank the anonymous peer reviewers who provided comments on

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Library of Congress Cataloging-in-Publication Data

Names: Wixom, Barbara Haley, 1969– author. | Beath, Cynthia Mathis, 1944– author. | Owens,

Leslie (Leslie Ann), 1972– author.

Title: Data is everybody’s business : the fundamentals of data monetization / Barbara H. Wixom,

Cynthia M. Beath, and Leslie Owens.

Description: Cambridge, Massachusetts : The MIT Press, [2023] | Series: Management on the

cutting edge | Includes bibliographical references and index.

Identifiers: LCCN 2022055462 (print) | LCCN 2022055463 (ebook) | ISBN 9780262048217

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Subjects: LCSH: Electronic data processing—Economic aspects. | Big data—Economic aspects. |

Data mining—Economic aspects.

Classification: LCC HF5548.2 .W59 2023 (print) | LCC HF5548.2 (ebook) | DDC 658/.05—

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LC record available at https://lccn.loc.gov/2022055462

LC ebook record available at https://lccn.loc.gov/2022055463

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Contents
Series Foreword
Foreword
  Introduction: Data Is Everybody’s Business
1  Data Monetization
2  Data Monetization Capabilities
3  Improving with Data
4  Wrapping with Data
5  Selling Information Solutions
6  Creating a Data Democracy
7  Data Monetization Strategy
8  Monetizing Your Data
Appendix: The Capability Assessment Worksheet
Acknowledgments
Notes
Index

Series Foreword

The world does not lack for management ideas. Thousands of researchers,

practitioners, and other experts produce tens of thousands of articles, books,

papers, posts, and podcasts each year. But only a scant few promise to truly

move the needle on practice, and fewer still dare to reach into the future of

what management will become. It is this rare breed of idea—meaningful to

practice, grounded in evidence, and built for the future—that we seek to

present in this series.

Abbie Lundberg

Editor in chief

MIT Sloan Management Review

Foreword

When Professor Barbara Wixom (Barb) joined MIT Sloan Center for

Information Systems Research (MIT CISR) as a principal research scientist

to lead the data research stream, she advocated for an advisory board of

senior global data leaders to help her. That’s us. Barb needed data experts

from a wide variety of organizations to participate in studies, set research

priorities, and vet insights. Our charge was to help keep the center’s data

research relevant, edgy, and applicable.

We share our passion for all things data via chat, while visiting the MIT

campus, in executive education sessions, and in virtual meetups. Through

these interactions, we have gotten to know and trust Barb, as well as her

MIT CISR collaborators, Cynthia and Leslie, and each other. The teamwork

and sharing tend to spark ideas and new directions. When one of us has

something particularly unique underway, the MIT CISR team investigates

what we are doing and helps us find the “aha!” moments. They write up our

efforts as a case study when our approach might help others succeed.

In Q1 of 2021, Barb kicked off a virtual conversation for the board called

“Inspiring Hearts and Minds.” She asked us to reflect: why is data different

today versus when we all started in the field? And because of that

difference, what should we be doing as data leaders? She hypothesized that

data leaders had to become much more evangelical; she thought we were

carrying too much of the responsibility for our organizations’ data assets.

And, to communicate persuasively, data leaders needed a simple, everyday

business language that a broad base of people could understand. We agreed.

We felt it too.

In our ensuing conversation, we agreed that data needs to become an

expected competence for the majority, an evolution that requires patience,

commitment, and ongoing investment in talent and new ways of working.

To build and keep momentum, we’ve found that you need the diverse

perspectives (especially regarding customer needs) of a wide range of

people, from different functions, disciplines, generations, and levels, who

are personally invested.

At this point, we don’t need to persuade people that data has value.

Instead, we need to help them contribute to the shift from tactical, local

efforts to building enterprise-wide capabilities. We’ve found that when

people see data as within their purview (not a responsibility of a few or an

IT department), they spot innovation opportunities more readily. As data

leaders, we focus on generating and sharing trusted data assets and leading

data literacy programs. Ideally, because of our efforts, most of our

colleagues will feel comfortable using data to improve work or products.

Many of us already apply MIT CISR research in our organizations and

can point to the benefits. Now, we welcome having a book that pulls the

research together in a way that appeals to all. We plan to distribute and

discuss this book across our organizations, coach others in the easy-to-use

frameworks, and inspire our people to participate in data monetization to

• build data assets,

• create novel, data-driven ways of working and customer

experiences, and

• learn about data and share knowledge about data with colleagues.

We hope to rally our colleagues across the enterprise—from our frontline

employees to our senior leaders and board members—to embrace the idea

that data is everybody’s business. This book is for everybody.

—MIT CISR Data Research Advisory Board Members

Introduction: Data Is Everybody’s Business

Everyone will say that data is extremely important to the business. However, beyond

that, people don’t know what to do next.

—Mihir Shah, Fidelity Investments

It’s common for leaders who want to create value from data to look for

inspiration within companies like Google. They might go on a road trip and

tour Google’s California offices. While there, they might encounter

advanced technology and brilliant data scientists developing proprietary

artificial intelligence–based products, such as a map that automatically

updates when a business is open.1 But what else is behind a company like

Google’s success? It’s several things. It’s the expectation that everybody is

a data practitioner, inventing new data-driven work practices and sharing

them with others. It’s an environment where data has been converted into

data assets that people can find, trust, and use to address unmet business

needs without having to create manual, bespoke processes and controls.

And it’s a firm-wide push to convert such data assets into revenues since,

after all, the mission of Alphabet Inc. (Google’s parent company) is to

“organize the world’s information and make it universally accessible and

useful.”2

Like newlyweds returning from a fantastic honeymoon, leaders from

traditional companies might return to their offices after a field trip to Silicon

Valley and feel overwhelmed by what’s ahead. Of course, few organizations

have as much data as Google. But all organizations, including yours, have

lots of data. It can be internal (e.g., accounting data) or external (e.g.,

purchased data about consumer credit risk or household preferences). It can

be structured (e.g., customer orders) or unstructured (e.g., tweets). It could

live in a spreadsheet, the cloud of a consultant, an email archive, a data

warehouse, or a data lake, to name just a few spots. Organizations today are

great at amassing data, causing a data deluge that grows with every

technology advancement in storage, processing, electronics, networking,

and telecommunications.

Yes, in most organizations, data is everywhere. Yet typically, the data is

tied to some context.3 The data is shaped and constrained by the processes

that create it and govern it. It is stuck in closed platforms, replicated in

multiple locations, incomplete, inaccurate, and poorly defined. As a result,

organizations focus a lot of managerial attention on liberating data from

silos and applying it to new specific uses such as calculating customer

churn or spotting supply chain breaks. Such efforts are complicated and

filled with friction, and each time a new chance to use the data emerges,

somebody must overcome the same hurdles. Leveraging data to meet an

unanticipated challenge or opportunity seems like a Herculean feat.

Companies like Google take a different approach with their data. They

decontextualize data and prepare data assets that can be accessed and reused

for innumerable purposes. Such data assets are accurate, complete, current,

standardized, searchable, and understandable—assets that everybody across

the company can easily incorporate into their value-creating initiatives. The

“data” in the title of this book refers to data assets. The book will describe

how organizations develop data assets so they can be exploited repeatedly.

The use of “everybody” in the title also has significance. Data is not just

for people with “data” in their job title. For organizations to develop data

assets and exploit them over and over, many more people need to be in the

mix. Just as an organization’s financial results are the responsibility of more

than finance and accounting staff, just as customer retention requires more

than sales, and just as talent management is not solely owned by human

resources (HR), data responsibilities need to be held well beyond the data

teams.

But why does the title say that data is everybody’s “business?” Because

your organization should be using data to make money and save money.

The financial inflows generated from your organization’s collective data

investments should be larger than your financial outflows. If your

organization does not actively manage the amount of money you generate

from your data—that is, the extent to which you monetize your data—you

will limit your financial inflows. Worst case, you will lose money. The

concept of data monetization is a fundamental business concept that will be

explained in chapter 1.

Clear, Memorable, and Integrated Frameworks

Leaders often find themselves trying to develop people’s “data savvy”

without much support. Unlike other management topics that are well

covered in business schools and executive education, the field of data is

relatively new, and standards and core curriculum are still emerging.4 The

Data Management Association, for example, published the first edition of

its data management body of knowledge in 2009.5 As a result, organizations

have had to develop their own data training materials, create their own

terminology, and engage in a lot of trial and error to get stuff right.

This book offers simple language and integrated concepts that can

quickly raise people’s data savvy. There are three key frameworks, shown

in figure 0.1. After reading this book, any reader should be able to pick up a

marker and draw the frameworks on a whiteboard as context for data

monetization conversations.

The first framework summarizes the five data monetization capabilities

organizations use to develop data assets and make data monetization fast

and successful: data management, data platform, data science, customer

understanding, and acceptable data use. The capabilities are depicted as a

fan because they are closely related and work together. Capabilities are the

expertise developed from mastering practices to decontextualize data:

divorcing it from a specific condition or context and turning it into reusable

data assets. This expertise may be embodied in people (as abilities or

proficiencies) or in other material resources (tools, routines, technology,

forms, policies, and so forth). Over time, data monetization practices build

the organization’s muscle in the five data monetization capabilities.

Figure 0.1

Three key data monetization frameworks

The second framework describes three kinds of initiatives in which

organizations invest that generate financial returns from their data assets:

improving work, wrapping products with data-fueled features and

experiences, and selling information solutions. Each approach uses data

differently and has an ideal owner, a set of risks to mitigate, and unique

outcomes. Recognizing what makes these three approaches different is

crucially important; trying to run a selling initiative as if it were an

improving initiative can be disastrous. It would be like fixing a kitchen

appliance with the tools, talent, and expertise needed for landscaping the

yard. Organizations that understand the distinct requirements of each data

monetization approach can make the right investments, set realistic

expectations, and generate optimal returns.

The third framework offers a way to think about organizational design

for data monetization. It describes five organizational connections between

domain experts and data experts. The term domain refers to an area of

subject matter that is valued in an organization, not including subject matter

related to data. (For example, accounting, marketing, nursing, teaching, and

law enforcement are common domains.) Organizations can’t expect people

to embrace and engage with data monetization on their own—think about

how busy people are simply trying to keep up with business as usual! To

encourage changes in people’s behaviors and (ideally) habits, organizations

must actively establish connections between domain and data experts so

that people share knowledge, learn, and, ultimately, change things. When

people across your organization know how to leverage data assets and data

monetization capabilities to innovate, and when they participate in and take

responsibility for data monetization initiatives, then you know your

organization’s connections are working.

Collectively, the three frameworks work together. They reinforce each

other, working like a flywheel to produce positive momentum that picks up

over time. To apply the frameworks, you can begin anywhere. As you better

understand and build data monetization capabilities, you enable more and

different kinds of initiatives and you engage more people across the

organization. As you become more proficient at deploying and generating

returns from data monetization initiatives, you satisfy and excite data

monetization investors, participants, and benefactors. And as you activate

connections between domain and data experts all over the organization, you

grow the population of stakeholders who will build and use data

monetization capabilities and participate in initiatives.

Who Should Read This Book?

This book is for . . . everybody! That is, everybody who works in an

organization. It has been designed to be relevant to audiences at all levels of

data expertise and to appeal to people in organizations large and small,

commercial and noncommercial, national and global. Yes, even

philanthropic and public organizations engage in data monetization. This

book can help you—and it can help you help others. It is as relevant to

leaders who manage their organization’s data monetization strategy as it is

to the people who bring data monetization principles to life.

This book is not a list of clever ways to monetize data, although the

examples in this book might spark some ideas for you. It bypasses the pros

and cons of different data vendors or data architectures. Instead, this book

will help you articulate your ideas and move them to fruition. You will learn

how to monetize data successfully by focusing on a few essential

frameworks.

About the Research

The authors of this book are affiliated with the MIT Center for Information

Systems Research (MIT CISR), a global nonprofit research center nested

within the MIT Sloan School of Management. Founded in 1974, MIT CISR

is committed to helping organizational leaders manage technology

(including data) successfully. The center produces relevant academic

research for leaders grappling with contemporary technology management

challenges.

The academic researchers at MIT CISR seek to identify and understand a

phenomenon to explain and predict outcomes. The research behind this

book has examined how organizations generate value from data from many

angles over several decades. The organizations studied range from ones as

large as Microsoft to some as small as thirty-person start-up AdJuggler, as

diverse as airlines, high-end retailers, and data aggregators, both

commercial and noncommercial, including government agencies and

nonprofits, located all over the world. The theoretical foundations of the

research focus on organizations and the people in them, not computer

science.

MIT CISR research often starts with exploratory qualitative studies—

case studies and field observations—to understand the problems

organizations have, what solutions work, and what solutions don’t. Many of

those case studies appear in this book. Qualitative studies are followed by

confirmatory quantitative analyses, using interview and survey data.

Readers will find the results of those studies in this book. A variety of

theories are used to develop the research insights. In some cases,

collaborators with deep expertise (from MIT and other universities around

the world) help formulate new ideas or extend current thinking; at other

times, concepts from the marketing and management literatures are

borrowed and reapplied.

The research behind this book was carried out alongside practitioners,

data practitioners in particular. In 2015, MIT CISR initiated a data research

advisory board of chief data officers and chief analytics officers from MIT

CISR member organizations. These one-hundred-plus practitioners not only

patiently complete extensive surveys and participate in interviews but also

prioritize topics to be studied, debate research findings, and test out

frameworks. Their voices can be heard throughout this book.

How This Book Will Unfold

The book opens (chapter 1) by defining data monetization and a few other

foundational concepts—such as the data-insight-action process, value

creation, and value realization—that will reappear throughout the book.

Next, chapter 2 describes five enterprise capabilities that organizations need

for data monetization to succeed as well as how organizations build these

capabilities. Then, three chapters (chapters 3, 4, and 5) give in-depth looks

at the three kinds of initiatives you can use to monetize data: improving,

wrapping, and selling. You will explore the critical success factors for each

data monetization approach and learn how to create and realize value using

each one. In chapter 6, you will learn how to engage more people in your

organization in the work of data monetization. This work includes creating

connections and incentivizing people to interact and reuse data assets.

Chapter 7 describes the importance of establishing a data monetization

strategy and presents four strategy archetypes representing four different

ways to monetize data. Finally, chapter 8 will encourage you to make it

your business to monetize data.

Each chapter starts with questions for you to ask yourself. Research

findings and definitions of key terms are presented along the way. The

chapters offer in-depth case studies that contextualize the purpose and

application of the frameworks. Finally, each chapter ends with a Time to

Reflect section to help you apply its lessons and concepts to your own

situation. Enjoy the read!

Data Monetization

If I cannot articulate the value of an initiative in a monetized way, it’s a wish list; it’s

nothing but a wish list.

—Jeevan Rebba, Otsuka Pharmaceutical Companies

Work has changed profoundly in the past few years. Managers are

introducing new work practices that help employees innovate, not just

punch the clock. For example, customer journey mapping initiatives help

people understand customer perspectives and improve customer

experiences; design thinking inspires people to solve problems creatively

and to enhance products in appealing ways; and test and learn processes

support people in taking small risks with small ideas that have the potential

to develop into something big.

These new ways of working have created an opening for individual

employees to contribute directly to organizational success. Regardless of

what team they represent or how senior their role is, employees today are

more attentive to how their work impacts the whole organization and how

changes to their work might pay off. At CarMax, for example, employees

throughout the company can link their work to one of CarMax’s missions:

either they are trying to sell more cars, or they are trying to buy more cars.1

As a result of these clear goals, creative people across the company can

unleash ideas on how changes to the tasks they perform could help achieve

the CarMax mission. A salesperson, for example, could hypothesize that an

improvement in how sales leads are identified would get more cars sold, run

a local experiment, and demonstrate that the idea worked.

The data assets available to employees at modern organizations like

CarMax play a significant role in these new ways of working. Data assets

provide a single source of truth; they draw on more data and novel data,

often sourced from social media, mobile devices, artificial intelligence (AI),

and the Internet of Things (IoT). People at these organizations use data

assets to measure, validate, inform, persuade, and prompt—in fiscally and

socially responsible ways. Data assets are built to be monetized.

This book features many examples of people in organizations actively

pursuing data monetization. In the last decade, Microsoft used data to move

its business model from being product based to cloud services based and

saw its stock price soar.2 Banco Bilbao Vizcaya Argentaria (BBVA), a

financial services company, used data to become a digital-first financial

services provider. As of 2021, it had won Forrester’s award for overall

mobile banking digital experience in Europe five years in a row.3 Finally,

PepsiCo used data to identify and serve granular market needs and

transformed its transactional retailer relationships into collaborative

partnerships.4 These three organizations and their data monetization

journeys are featured in detail in chapters 2, 3, and 4, respectively. So, what

is data monetization?

Data monetization is turning data into money. Money is a crucial

resource for all organizations, public and private. Organizations need

money from customers, donors, or citizens, and they need to handle that

money prudently. Organizations use data not only to create valuable

benefits—customer and employee satisfaction, brand capital, desired

product enhancements, streamlined processes, or citizen welfare—but also

to purposefully realize financial value—money—to improve their bottom

line.

Data monetization is the generation of financial returns from data

assets.

These days, different kinds of organizations pay attention to many

different “bottom lines.” What number tells the world that your

organization is sustainably efficient and effective? It might be your net cash

flow, your net income, your unrestricted net income (if you are a nonprofit),

or some other measure of efficiency and effectiveness. In this book, bottom

line means the difference between money in and money out.

There is a world of difference between creating valuable benefits and

turning those benefits into money. We will call the first value creation. By

that, we mean creating benefits that are desirable and have the potential to

reach the bottom line. These benefits are common goals in data initiatives:

more streamlined processes, smoother supply chains, employee satisfaction,

or products that customers find desirable. This book is mainly about how to

create value from data.

We will call the second value realization. By that, we mean transforming

the value created by these initiatives into money, or, simply, more money in

or less money out. Value realization brings data monetization home.

Realizing value from data is about converting value created—efficiency or

customer value—into money or getting money directly from data by selling

it. The ultimate goal of data monetization is bottom line improvement—

reducing costs or growing earnings. Staying focused on realizing value

increases the odds that your data investments will pay off and that the

organization will not leave money on the table. So, even though this book is

mainly about how to create value, the imperative to realize any value you

create should always be kept in mind.

Questions to ask yourself

How does your organization create value using data today? How well

does your organization report on the financial returns that result from

these value-creating efforts? Can you track how much money data

contributes to your organization’s bottom line?

Research to consider

High-performing organizations—in terms of profitability, revenue

growth, innovation, and agility—report that data monetization accounts

for 10 percent more of their overall revenues than it does at their low-

performing peers.5

Creating Value from Data

Over the past several decades, organizations have learned a lot about

creating value from data. But arguably, the most important lesson is that

creating value from data requires that a person or a system take some action

it otherwise would not have taken. Data needs to be used to change the way

something is done or to produce something new. Better processes and

products create new value, not the data itself. This understanding is core to

the data value-creation process, commonly referred to as data-insight-

action. During this process, data is used by people (or systems) to produce

an insight, the insight informs an action, and the action results in a valuable

outcome. As figure 1.1 illustrates, data, insight, and action must all happen

before value creation can occur: to grow valuable fruit, a fruit tree needs

adequate soil and nutrients, the right amount of sun, and careful watering. If

the data value-creation process breaks down or stalls—maybe there’s a

well-planted seed but no light or no water—then the associated investments

in that little plant are merely sunk costs. This idea that the complete data-

insight-action process precedes the creation of value is a fundamental data

monetization idea that you may have encountered at a data-related

conference, course, or event. It’s a central idea in this book.

The data value-creation process (a.k.a. the data-insight-action

process) occurs when people or systems use data to develop insights

that inform action, which generates value.

Figure 1.1

The data value-creation process (a.k.a. the data-insight-action process)

Realizing Value from Data

Creating value from data is necessary but not sufficient. The final step is

making sure that whatever value is created—something better, something

new—contributes to the organization’s bottom line. In other words, the

“value created” needs to be turned into money. This step is value

realization. Unless financial value is realized, data has not been monetized

and the organization is now more costly.

Value realization occurs when value that has been created from data is

turned into money.

Think about it. Fruit doesn’t pick itself; when fruit stays on the tree, it is

neither eaten nor sold. As figure 1.2 illustrates, the whole point of

cultivating a fruit tree is for someone or some people to enjoy its fruits.

Value can be realized in one step or two steps. In the one-step process,

data is exchanged for money in some form, and the money you receive for

the data you sell is real and ready to count. Imagine an information business

such as Nielsen selling consumer behavior data to a television network.

Nielsen sets a price for the data; the network pays it. The data’s value is

realized with the sales transaction.

Figure 1.2

Value realization

Value realization is a two-step process if it entails first creating

something that has inherent value and then, second, cashing in that value so

that it becomes financial value. The first step of creating value is the result

of a data monetization initiative, but the second step, value realization, may

require the involvement of multiple stakeholders. Step two could be the

responsibility of a senior manager or leader if it requires higher levels of

authority. For example, say a recruiting team uses data to make the new hire

process more efficient. That’s step one, and the efficiencies are inherently

valuable. The crucial second step occurs when the efficiency gets turned

into money by, say, cutting headcount and reducing the budget for

onboarding new hires. When organizations have more resources than they

need to sustain routine operations, that’s referred to as “slack.” Thus, in the

second step, the slack created by the new, more efficient process is

removed, making the organization better off financially.

Maybe a different data monetization initiative delivers a product

enhancement that customers value. In that case, the second step requires

that the product owner increase the product’s price to reflect its higher

value, allowing additional revenues to flow to the income statement.

The second step, realizing value, is often the tricky one. Cutting budgets

and repricing products is not something that just anyone can do. Suppose

the organization is not pushing to cut expenses or is reluctant to increase

prices. In that case, it might be easier for a process owner to just let slack be

absorbed by employees or for a product owner to let the value of product

improvements go home with customers. It might even be desirable to do so

in some circumstances. But if the slack that arises from a data monetization

initiative isn’t removed or if the additional value of a product is not

extracted from customers, the data monetization initiative will not

contribute to the organization’s bottom line.6 The initiative cannot claim to

have monetized data.

Figure 1.3

Three approaches to data monetization

Three Approaches to Data Monetization

There are three distinct approaches that organizations can use to monetize

data, as shown in figure 1.3.

Improving uses data to create efficiencies in work from better, cheaper, or

faster operations. Realizing value requires removing or redirecting the slack

created by the efficiencies that, ideally, flow to the organization’s bottom

line.

Wrapping uses data to enhance products such that customers want to buy

more or are willing to pay more. Value realization requires raising prices or

selling more of the products to improve the bottom line.

Selling is the exchange of an information solution for some form of

money. In this way, realizing value is straightforward and appears in the

form of new financial inflows.

Research to consider

In 2018, 315 executives were asked whether their organizations were

creating value via improving, wrapping, or selling. Across the sample, 50

percent strongly agreed that they were creating value from improving, 33

percent strongly agreed they were creating value from wrapping, but

only 19 percent strongly agreed they were creating value from selling.7

Improving

Improving is the most common way that organizations monetize data, and

examples abound. The United Parcel Service (UPS) used vehicle route data

to optimize delivery routes to save US$400 million annually.8 Columbia

Sportswear used historical package-tracking data to eliminate root cause

issues in their supply chain. This decreased both out-of-stock and

overstocking problems, saving more than US$27 million in inventory

costs.9 Trinity Health used data from smart hospital beds to speed up nurse

response time by 57 percent. Leaders at Trinity Health associated the nurse

response time improvement with a reduction in patient falls, likely

decreasing the cost of patient care.10

Improving is a data monetization approach that generates money

when organizations use data to change the economics of work for the

better and then remove or redirect the resulting slack.

Most organizations have experience using data to improve business

processes and work tasks. Many were inspired by the Business Process

Reengineering (BPR) movement in the 1990s that encouraged organizations

to analyze and design efficient workflows and business processes.11 To

execute BPR, organizations applied technology to clean data and boost its

availability. The data was then used to analyze the root cause of process

slowdowns, to measure the benefits of moving from an old way of work to

a new, more radical way, and to monitor and manage critical process

metrics. The BPR movement convinced many organizations of the benefits

of data-driven process or task improvements. But a negative result of this

movement was that many organizations got in the habit of assuming that the

benefits of process improvement would make their way to their bottom

lines. In fact, it takes organizational attention, resources, and discipline for

financial value to materialize from efforts like those at UPS, Columbia

Sportswear, and Trinity Health.

Quantifying the outcome from improving takes two steps. First,

organizations need to measure the uplift in efficiencies or quality that result

from improving with data. Second, they must remove or redirect the slack

created by those efficiencies or productivity improvements. Some

complications make the value of efficiencies hard to turn into money:

sometimes, an improvement in one process creates efficiencies in

downstream processes; or slack created by an improvement is used to

relieve overworked or stressed employees; or efficiencies gained in a

process show up in increased production or reduced inventory rather than

slack. But if an improvement is supposed to reduce costs or budgets and

they don’t change, data is not being monetized.

Wrapping

The second data monetization approach is wrapping. Wrapping initiatives

create data-fueled features and experiences to increase the customer value

proposition of a product. When we say “product” in this book, we’re

referring to the thing an organization delivers to a customer that satisfies

their needs. This might be a good or a service, it might be virtual or

physical, or some combination of all of these. We use the terms “product”

and “offering” to refer to whatever is delivered to the customer.12 To realize

some of the value created by an enhanced product, the organization must

raise the product’s price or sell more of it. “Selling more of it” could mean

selling more units of the same offering to an existing customer, selling more

related offerings, selling to additional customers, or sustaining sales of a

product whose sales are eroding.

Wrapping is a data monetization approach that generates money when

organizations use data-fueled features or experiences to enhance the

value proposition of a product and then raise prices or sell more

products.

Opportunities to wrap products are everywhere; look around at the surge

in connected devices and the new personalized ways organizations engage

with customers. Some examples of wrapping include adding information to

offerings in the form of reports, alerts, scores, visualizations, or dashboards

that complement or enhance the offering and make it more appealing to

customers.

The wrapping approach to data monetization can create distinctive

offerings in the marketplace. Consider Schindler, an elevator maker. The

company complements its elevators with an equipment performance

dashboard to help building managers monitor elevator performance.

Intergovernmental organizations like the World Bank provide portals for

contributing governments to show them that their donations are achieving

their desired philanthropic goals. Healthcare insurers add visualizations for

health plan administrators to help manage their healthcare costs. In each

case, an offering—an elevator, a philanthropic initiative, an insurance plan

—is wrapped with data, insights, or action to make them even more

attractive to customers (or other constituents).

Any offering can be wrapped, even diapers!13 Pampers is one of several

diaper companies that developed sensors to attach to diapers that send a

mobile alert via an app to parents when the diaper is wet. The app also

tracks information about the baby’s sleep and wake times. It’s hard to

imagine a product that could not be wrapped.

In the digital era, organizations are expected to wrap their offerings using

data-fueled features and experiences that delight customers. But too often,

they assume that they are realizing value from those efforts—without

verifying whether and how much money hits their books. As it does with

improving, data monetization by wrapping takes organizational attention,

resources, and discipline. It requires that organizations first gauge the extent

to which customers regard the product more positively because of the wrap.

For example, do customer loyalty scores go up? Do customers recommend

the product more enthusiastically? Second, organizations need to cash in on

this positive regard by extracting revenues from customers, for instance, by

raising the price of the offering.

There are also complications with data monetization by wrapping:

sometimes an enhanced offering has already been paid for, and the

opportunity to raise prices will only arise in the future. Sometimes

wrapping staves off competitive pressure on prices or increases switching

costs, thus retaining customers who might otherwise have been lost. In

those cases, there might not be an immediate lift in sales but instead a future

lift in sales or dampened sales erosion. However, none of these

complications are reasons to ignore the step of value realization. Wrapping

must make financial sense, or the associated investments might have been

made more profitably elsewhere.

Selling

Companies have been monetizing data by selling information for decades.

Consider the retail industry: retailers have sold their point-of-sale (POS)

transaction data to companies like IRI since the late 1970s.14 IRI, in turn,

sold aggregated data and analytics back to retailers (and other

organizations) that wanted to better understand their product sales

compared to those of their competitors.15 POS data is an important raw

material for the aggregator because they can generate a substantial revenue

stream from it.

Selling is a data monetization approach that generates new revenues

when organizations commercialize data in the form of an information

solution.

Instead of purchasing reports or metrics from the aggregators, some

retailers exchanged their POS data for these solutions. This is bartering,

which is a financial transaction. In fact, both retailers and aggregators must

regularly assess whether the tender they receive for their POS data and

analytic reports justifies the expense or risk of the exchange.

This book will refer to a data product as an “information solution” to

distinguish it from other kinds of products. Information solutions are

standalone offerings that solve compelling customer problems. Customers

might buy an information solution because it includes scarce data that they

need, because it will help them get their own products to market faster,

because of its sophisticated algorithms, or because the interface is user-

friendly, to name just a few reasons. A subscription to Bloomberg news,

data, and trading tools is an example of an information solution. Another

example is IBM’s Weather Company data application programming

interfaces (APIs), which serve climate, environment and forecast data from

a cloud-based platform.16 Note that selling is one of three approaches to

data monetization, yet too many organizations may naively view selling

their data as the only way to monetize it. When organizations limit what

they view as a data monetization opportunity, they end up leaving money on

the table. A lot of money.

The Improve-Wrap-Sell Framework

All three data monetization approaches—improving, wrapping, and selling

—are distinct ways of turning data into money. Their differences are

summarized in table 1.1. Together, the three approaches make up the

improve-wrap-sell framework. Regardless of industry, business model, size,

geographic location, or strategic intent, your organization can monetize data

using some combination of improving, wrapping, and selling. In chapter 7,

you will read about how to choose what combination of approaches is right

for your organization.

Because of the distinctions in how initiatives create value, they require

different capabilities, demand different owners, entail distinct risks, and call

for unique metrics and measurement methods. This will be covered in detail

in chapters 3, 4, and 5. In brief, the success of improving depends on the

leadership of process owners who define needed changes, recognize the

opportunity for applying data, and ensure the adoption of changes.

Wrapping demands engaged product owners who can envision the value

offered by data to their products. They must be willing to engage with other

parts of the company—like IT and customer service—in ways that were not

necessary when developing and selling the core product. Finally, selling

requires the identification of an entrepreneurial leader who can envision and

launch a new information solution for new customers.

Table 1.1

Three approaches to data monetization

Improving Wrapping Selling

Value-

creation

process

Data creates

efficiencies (and thus

slack) by making

operational processes

or tasks better, faster,

and cheaper.

Data

enhances the

customer

value

proposition of

products.

Data is

commercialized

and sold in the

form of

information

solutions.

Improving Wrapping Selling

Value-

realization

process

Slack is eliminated or

redirected.

Customers

pay more or

buy more.

New revenue

streams are

generated.

Measure

value

realization

via

Impact on the bottom line

Who is

accountable

for

outcomes

Process owner Product

owner

Information

solution owner

Key risks Lack of action taking

and value creation

Negative

impact on

customer

value

proposition

when a wrap

falls short

Inability to

create or

sustain

competitive

advantage

Source: Barbara H. Wixom and Jeanne W. Ross, “Profiting from the Data Deluge,” MIT Sloan

Center for Information Systems Research, Research Briefing, vol. XV, no. 12, December 17, 2015,

https://cisr.mit.edu/publication/2015_1201_DataDeluge_WixomRoss (accessed January 10, 2023).

Similarly, the three approaches entail distinctly different risks. The risk

with improving is that the data-insight-action process will break down and

value will not be created. Process owners are ideally suited to manage this

risk by carefully tracking likely value creation and making course

corrections. The risk associated with wrapping is already familiar to product

owners: that the product enhancement adversely affects the value the

customer enjoys from the core offering. Product owners, fortunately,

already know how to track customer satisfaction; they will need to follow

similar protocols when implementing wraps. Finally, the risk associated

with selling is the risk that accompanies any new business venture: business

failure due to the inability to create or sustain competitive advantage.

Information solution owners must remain highly sensitive to competitive

pressures from substitutes and new entrants.

A Final Appeal for the Term Monetization

All organizations should make sure their data investments pay off. And

today, data investments can be enormous. Data investments could simply

make the organization more costly to run if no one is making sure that value

is being created and realized. As a basic business principle, organizations

should generate more money from their data assets than they invest in

producing and managing them. If you buy into the concept of data

monetization but simply don’t like the term data monetization, you are not

alone. It’s distasteful for some. Some organizations, especially

noncommercial ones, have little appetite for the term. This likely comes

from leaders associating data monetization with going too far with data,

with unacceptable exploitation of data assets, or with data trickery or

sneaky tactics.

If you really can’t bring yourself to use the term right now, call it what

you want. Just make sure you use a term that links data to your

organization’s bottom line. It helps to have clear, shared language that

people can use in discourse and debate. If everybody in your organization

uses the term data monetization to mean the same thing, you should be able

to have fewer discussions about whether it’s ethical to monetize data and

more discussions about how to monetize data ethically.

Time to Reflect

The status quo in most organizations is a flurry of data activity but no

coherent vision of what it means to monetize data. Here are the key points

from this chapter to keep in mind:

• As shown in figure 1.2, the fruit (the value) emerges at the end of

the value-creation process. What kind of value do your data

initiatives most frequently create? How well are you measuring

data-fueled value creation today?

• Assuming a data initiative creates some value (there is fruit on the

tree), that value should be realized (the fruit should be taken off

the tree) by moving it to the firm’s bottom line. Do you just

assume that value is realized from your data initiatives, or do you

know it made it to your bottom line?

• There are three basic ways to monetize data: using it to improve

work, using it to wrap goods and services (“products”), or selling

an information solution to someone else. Can you think of an

opportunity to use each one of these approaches in your

organization?

• Improving, wrapping, and selling initiatives should not all be

managed the same way. Who owns your different improving,

wrapping, and selling initiatives? They’re not all owned by IT, are

they?

• Organizations should generate bottom-line impact from their

investments in data. How comfortable is everybody in your

organization with using the term data monetization to describe

this?

Data monetization is an incredible opportunity for organizations. But it’s

not simple. For one, it requires specific capabilities that help organizations

create widely accessible data assets. In the next chapter, you will read about

those capabilities.

Data Monetization Capabilities

In many companies, any business unit has the autonomy to hire a firm to [help them]

grab data, toss it in a data store of their choice, and slap an individual use case on top of

it. This happens over and over and over. It takes a different mindset and approach to

build capabilities for consistency across a variety of use cases.

—Brandon Hootman, Caterpillar, Inc.

Why can some organizations monetize their data again and again, while

others have hit-or-miss results? An organization that consistently monetizes

data is leveraging some robust enterprise data monetization capabilities.

This chapter covers how to build the five capabilities that produce data

assets that are accurate, available, combinable, relevant, and secure. Data

assets with these characteristics are easily reused, and reusable data assets

lead to faster and cheaper data monetization initiatives. Organizations

recognized for excellence in data monetization don’t just engage their

advanced capabilities. They also prioritize making it easy for people

throughout the organization to access each capability.

Generally speaking, capabilities are the ability to do something.

Capabilities can exist at a foundational level or they can be advanced. For

example, most home cooks can boil an egg for breakfast; a professional

chef can take an egg and turn it into something unexpectedly delicious. A

diner may have line cooks on staff who consistently prepare a limited set of

menu items, whereas a restaurant known to have an advanced cooking

capability likely has a chef and supporting staff who can create a variety of

sophisticated dishes. The ability to cook at an advanced level is usually

acquired through education and experience, and it no doubt has an element

of talent to it.

In organizations, it’s common to bring people specializing in a particular

capability (e.g., accountants) together. Not only do they learn from each

other as they work together, but everybody in the organization also knows

where to find that capability (in accounting). If the ability to do something

is only needed in select parts of the organization, then the organization

simply needs to establish a local capability. For example, a global company

needs local experts in tax law in each country in which it operates. They are

highly valued in their geography, but their expertise doesn’t need to be

available to other countries.

When a capability is applicable across the organization, it should be an

enterprise capability. For example, an enterprise “digital asset

management” capability allows communications teams worldwide to pull

official brand images from a centralized system. Producing, approving, and

organizing such media content only has to happen once; the images can

then be used and reused in various sales and marketing contexts.

Questions to ask yourself

Can your organization turn data into accurate, available, combinable,

relevant, and secure assets that people can reuse? Or are there skills and

knowledge that are obviously missing?

Research to consider

Organizations who are top performers in data monetization outcomes

have capabilities that are about 1.5 times stronger than those of bottom

performers, and the top performers’ outcomes are 2.5 times better than

the bottom performers.1

The Five Data Monetization Capabilities

Data monetization capabilities are a collection of material resources and

abilities or proficiencies that organizations rely on to develop their data into

reusable data assets. A couple of decades ago, we studied information

businesses like Nielsen and IRI, which rely on data assets for their

economic survival, to understand their business models. It turned out that

the key to these information businesses was that they had five advanced

data monetization capabilities.2 As the research moved on to investigating

data monetization in other kinds of organizations, it became clear that these

five capabilities are key to any data monetization initiative at any

organization. Yes, any organization—including yours—needs data

management, data platform, data science, customer understanding, and

acceptable data use capabilities.

Figure 2.1 shows the five distinct data monetization capabilities laid out

in the shape of a fan. The five capabilities work together. While these

capabilities may not surprise you, they certainly are not easy for

organizations to master.

Here are descriptions of the five capabilities and what it means to achieve

an advanced level of each one. Later in the chapter, you will read about

capability building at the financial services company BBVA.

Data management A data management capability is the ability to

produce data assets that people can find, use, and trust. Organizations with

advanced data management capabilities can report on the accuracy of their

data, match related data entries, consolidate and streamline data fields, and

integrate related data from external sources, such as data aggregators and

suppliers.

Data platform A data platform capability is the ability to capture,

transform, and disseminate data assets securely and efficiently. It leverages

contemporary, cloud-based software to ingest, process, secure, integrate,

and deliver data assets. Organizations with advanced data platform

capabilities can cost-effectively distribute data assets inside and outside the

organization at scale.

Figure 2.1

Five data monetization capabilities

Data science A data science capability is the ability to use scientific

methods, processes, algorithms, and statistics to extract meaning and

insights from data assets. Organizations with advanced data science

capabilities support data-savvy people across the organization in making

evidence-based decisions. They leverage advanced statistics and techniques

such as machine learning to inform and automate processes and products.

Customer understanding A customer understanding capability is the

ability to gather accurate and actionable knowledge about customer needs

and behaviors. Organizations with advanced customer understanding

capabilities accurately grasp what customers need and value, can cocreate

with customers, and can formulate and test hypotheses about customer

preferences.

Acceptable data use An acceptable data use capability is an

organization’s ability to gather, store, and use data assets in ways that are

compliant with existing laws and regulations and consistent with

organizational and stakeholder values. Organizations with advanced

acceptable data use capabilities have contextualized norms and policies.

They have scalable oversight processes, which ensure that employees,

partners, and customers appropriately engage with organizational data

assets.

Capabilities are, by nature, fairly abstract. To make them less ambiguous,

let’s look at the specific practices that build up each capability.

Data Monetization Capabilities Accumulate from

the Practices You Adopt

Data monetization capabilities come mainly through learning by doing, and

so they are shaped by the practices you adopt. For example, when an

organization adopts a foundational practice such as customer journey

mapping, it gradually builds foundational data monetization capabilities,

such as the ability to gather knowledge about customer needs (customer

understanding). Once foundational practices are well established, more

complex practices can be adopted, leading to more learning and higher-

level capabilities. As a result, as illustrated in figure 2.2, as people and

systems undertake more and more complex data monetization practices,

their capabilities get more robust, and the fan becomes fully built out to its

edges. Your organization will progress from foundational to intermediate to

advanced capability levels as it adopts increasingly sophisticated capability-

building practices.3 There’s no shortcut to acquiring robust data

monetization capabilities; they are the result of steady work in the right

direction.

Figure 2.2

How organizations use practices to build capabilities

Take the data science capability as an example: Typically, organizations

first become proficient at basic reporting dashboards and visualization.

Next, they master statistical techniques and approaches. Then they learn

how to use machine learning and specialized analytics like natural language

processing. It’s nearly impossible to fast-track a data science capability by

pumping money into machine learning tools (an advanced practice). At

best, that would yield a pocket of underused machine learning tools that

most of the organization simply would be unable to exploit. An

organization needs to walk before it can run. People need to learn (and

apply what they are learning) as they progress from foundational to medium

to advanced practices.

The next section identifies practices that organizations adopt to build and

invigorate each data monetization capability.4 Practices come in many

different forms. They may be expressed as policies (“Cloud first!”) and

backed up with procedures for minimizing deviations from the policy. They

may be automated (programs for managing access to data), embedded in

tools (statistical packages or AI modeling tools), or expressed as rules and

routines (how customer feedback will be aggregated and shared). As you

will see, there are three levels of practice that have been associated with

building three levels of capability. There are no doubt other practices that

can substitute and achieve similar capability-building outcomes, but

research has validated the practices listed below.

Data management To build a data management capability, organizations

engage in practices that turn data into accurate, integrated, and curated data

assets.

• Master data (foundational): Practices that produce reusable data

assets include establishing automated data-quality processes,

identifying data sources and flows that describe core business

activities or key entities like customer and product, creating

standard definitions of priority organizational data fields, and

establishing metadata for those data fields.

• Integrated data (intermediate): Practices that allow data to be

integrated from both internal and external sources include

mapping and harmonizing data sources and standardizing,

matching, and joining data fields.

• Curated data (advanced): Organizations use taxonomy and

ontology to curate their data. These practices involve analyzing

data and its relationships, depicting data and its relationships in a

way that is accessible and meaningful to users, and maintaining

that depiction over time. These practices make it possible to

augment the organization’s data assets with data assets from

external sources or with data assets created as a byproduct of the

development of AI models.5

Data platform To build a data platform capability, organizations engage

in practices that allow them to draw on cloud, open source, and advanced

database technologies to produce software and hardware configurations that

satisfy their data processing, management, and delivery needs.

• Advanced tech (foundational): The adoption of cloud-native

technologies is an example of a data platform practice. Modern

database management tools include products that leverage

leading-edge techniques for data compression, storage,

optimization, and movement.

• Internal access (intermediate): The use of APIs to offer data and

analytics services internally is a practice that eases access to raw

data or data assets from any system.

• External access (advanced): APIs can also be used to make an

organization’s raw data or data assets available to external

channels, partners, and customers. Providing APIs to stakeholders

outside the organization requires adopting practices for certifying

external users and tracking their platform activity.

To give you a sense of what it looks like to engage in these data

management and data platform practices, consider Fidelity Investments, a

Boston-based financial services company. In 2019, the company kicked off

a multiyear effort to rationalize one-hundred-plus data warehouses and

analytics stores into a common analytics platform.6 Fidelity invested in data

management practices such as creating a common identifier for each major

data entity at the company, for example, customer, employee, and investible

security. It adopted practices for creating definitions of more than three

thousand company data elements and building a central taxonomy and

catalog to organize this new company terminology. Fidelity’s data platform

practices included installing a new modern, cloud-based analytics platform

to house, process, and serve up the company’s data assets to people across

Fidelity.

Data science To build a data science capability, organizations engage in

practices that advance their ability to use data science techniques and

thinking. They hire new talent and upskill and develop existing employees.

They invest in tools and methods that support data science work so that data

science tasks can be appropriately managed and scaled.

• Reporting (foundational): Practices that foster the use of

dashboards and reporting include standardizing data presentation

tools and designating which data assets will be regarded as the

“single source of truth” for process outcomes or business results.

They include educating employees about data storytelling and

evidence-based decision-making.

• Statistics (intermediate): Practices that promote the use of math

and statistics include selecting analytics tools, hiring people with

sophisticated mathematical and statistical knowledge, and

establishing data science support units. They include teaching

probability, statistics, and skills that increase the usability of

analytics tools and techniques.

• Machine learning (advanced): To promote the use of advanced

analytics techniques such as machine learning, natural language

processing, or image processing, organizations engage in feature

engineering, model training, and model management. They use

AI explanation practices that ensure AI models are value

generating, compliant, representative, and reliable.7

Customer understanding To build a customer understanding capability,

organizations connect with customers to collect data about them—

demographics, sentiments, context, usage, and desires—from which they

extract analytical insights about core and latent customer needs.

• Sensemaking (foundational): Listening to customers and making

sense of their needs is an example of a foundational customer

understanding practice. Customer-facing employees can help

organizations identify important customer needs by sharing ideas

via “suggestion boxes” or crowd-sourced innovation events.

These employees can also participate in agile or cross-functional

teams tasked with mapping customer journeys or designing new

products and processes.

• Cocreation (intermediate): Engaging customers in the cocreation

of new products or new processes requires practices for

identifying the appropriate customer, establishing the terms of

customer engagement, and making good use of customer time.

• Experimentation (advanced): Common practices for testing ideas

with customers include hypothesis testing (observing customer

behavior to see if it conforms to expectations) and the use of A/B

testing (using randomized experimentation with two variants, A

and B).

Beginning in 2015, Australian insurer IAG made a significant investment

in its data science and customer understanding capabilities when it acquired

the forty-person customer insights company Ambiata.8 In effect, IAG

acquired experienced data scientists, who brought an influx of data science

practices into the company—statistical techniques, machine learning, and

analytics methodologies. A year later, in December 2016, IAG created a

new division—Customer Labs—that merged experts in data, analytics,

marketing, customer experience, design thinking, and product innovation.

Customer Labs benefited from customer understanding practices that

Ambiata had mastered during its years as a customer insights company, like

A/B testing and experimentation.

It took several years for IAG to diffuse the Ambiata practices across the

enterprise (so that many IAG employees could contribute to and use the

advanced data science and customer understanding capabilities). The

company actively drove diffusion by using its new Customer Labs division

to test and refine Ambiata practices until they were a good fit for IAG.

Acceptable data use To build an acceptable data use capability,

organizations engage in practices that allow them to effectively address

regulatory and ethical concerns regarding data asset use by and about

employees, partners, and customers. Organizations draw on this capability

to mitigate the risk of using data assets inaccurately, undesirably, or in ways

that are not contractually or legally allowable.

• Internal oversight (foundational): Practices that ensure acceptable

use of data by employees usually begin with establishing data

ownership; training employees about laws, regulations, and

organizational policy; setting up data access approval processes;

and auditing employee data access.

• External oversight (intermediate): Practices that ensure the

appropriate use of data assets by partners begin with establishing

clear agreements about appropriate use with partners and end with

auditing partner use of data assets.

• Automation (advanced): Practices that allow customers to self-

manage their data begin with establishing policies regarding

customer control of data. These policies are then implemented

both by communicating the policies to customers and facilitating

customer control through automation. Automating practices also

helps organizations scale internal and external oversight activities.

In 2019, Anthem Health added to its acceptable data use capability by

hiring a technology and governance provider to stand up a cloud-based

environment in which Anthem could collaborate with start-ups, academics,

and others who wanted to use its deidentified patient health data set to

develop and validate AI models.9 There were big issues to resolve: data

access, development standards, intellectual property rights, and more. The

provider’s technology allowed Anthem to set up base contracts with

parameters that could be tweaked to accommodate the distinct needs of

each partner’s project; this made the up-front contracting process far more

straightforward.

Figure 2.3 depicts the capabilities fan with advanced states in all five

capabilities. When each capability is developed to around the same level,

the fan could be said to be “well-rounded.” Because the five data

monetization capabilities are highly complementary, they should ideally be

developed to similar levels of advancement. It will be difficult to fully

exploit an advanced data platform without also having advanced data

management and advanced data science capabilities in place. That said, the

five capabilities rarely grow at the same pace. Sometimes an organization

has developed one or two capabilities more fully than it has developed

others, making for a lopsided and possibly ineffective fan. It is likely to be

obvious where additional practices would strengthen the collective power of

the five capabilities.

Figure 2.3

Advanced states of the five data monetization capabilities

Assess Your Data Monetization Capabilities by Assessing Your

Practices

While capabilities are very difficult to measure precisely, practices can be

observed and assessed, and they are a good proxy for an organization’s

capabilities. Consider the earlier cooking example: it’s almost impossible to

look at two people and discern who is the amateur chef and who is the pro.

But you can easily recognize an advanced cook by watching her behavior in

the kitchen: how does she choose a knife, decide when meat is done, and

plate the food? These are actual, observable practices that roll up to a

credible evaluation of a person’s cooking capability.

You can use the capability assessment worksheet in the appendix to

evaluate your organization’s data monetization practices. Follow the

instructions to rate your practices and reveal your organization’s capability

levels.

Enterprise Capabilities Make Initiatives Faster

and Cheaper

Your organization may have considered adopting some of the practices

identified above for reasons of efficiency or cost without realizing their

value in terms of advanced data monetization capabilities. Someone might

push a practice (or policy or rule or process) for other reasons without

realizing that the practice will help build a data monetization capability. For

example, an organization might adopt a “cloud first” policy for financial

reasons without realizing that it is an important foundational practice for

making data assets available for reuse both internally and externally.

Organizations must adopt practices at the enterprise level if they are

going to build enterprise capabilities. Ideally, you want practices to

strengthen capabilities into ones that can be repeatedly shared across the

organization for use by any improving, wrapping, and selling initiative.

Cloud-native applications, for example, let developers quickly create and

deploy individual microservices without disrupting any other microservice;

the developers’ teammates can reuse what works. Adopting enterprise

customer understanding practices ensures that customer insights gained in

one part of the organization are captured and available to other areas. It can

take some time and effort for organizations to migrate local data capabilities

into an enterprise capability (consider how IAG slowly incorporated

practices from its acquisition into the greater IAG company), never mind to

grow new enterprise capabilities organically, from scratch. You can,

however, imagine the payoff as capabilities build up and are available to be

used pervasively and in new ways.

You can think about the value of enterprise capabilities within the context

of racing. Imagine that you own a Formula One racing team. You would

want to race your fancy car on some beautifully engineered track, with

luxurious stands and amenities for spectators, shared and secure fueling,

nicely fitted-out facilities for your pit crew (and your data scientists), and

carefully engineered security barriers to protect your driver. You’d expect

the race host to provide all those shared and reusable capabilities. Your time

would be spent designing the perfect car, finding and motivating the best

driver, and crafting a winning strategy.

In the best of all possible worlds, figure 2.4 shows what data

monetization initiative teams want from their organizations: excellent data

monetization capabilities—great data management to fuel the initiative, a

great data platform for a fast and smooth ride, great data science to optimize

the initiative, great customer understanding to make sure customers are well

served, and great acceptable data use to keep the initiative safely on course.

When enterprise capabilities serve the needs of initiatives, those initiatives

have a smoother, faster path. Teams are free to focus on the specifics of

their initiative: managing stakeholders, developing the team, and training

models.

Figure 2.4

The great enterprise capabilities that teams want from their organizations

Before your organization races ahead to build enterprise capabilities, it’s

important to note that capabilities only create value if they are used. In fact,

organizations can’t take too much time or spend too much money building

enterprise capabilities before they start leveraging them. Enterprise

capabilities that aren’t used are just sunk costs. Nevertheless, an

organization’s long-term goal is the perfect, fully equipped racetrack—a set

of advanced enterprise capabilities—that gets used over and over by many

and varied initiatives.

The reality is that most practices are initially adopted by initiative teams,

so most data monetization capabilities are developed in the context of

initiatives. When enterprise capabilities do not exist, the initiative owner

must unearth the capabilities needed to meet her objectives. Her initiative

team might try out a new tool, experiment with a cloud platform, or work

out an acceptable use policy just because they need to conclude their

particular initiative successfully. However, when an organization adopts the

data monetization practices it needs initiative by initiative, it can end up

with local capabilities that are hard to leverage elsewhere. It takes some

vision and leadership to accumulate enterprise capabilities. This perspective

may be found in the C-suite or a chief data office, where a top-down, global

mindset is expected.

Building Enterprise Data Monetization

Capabilities at BBVA

Let’s look at an example of how one organization built its enterprise data

monetization capabilities to advanced levels over time. BBVA has been

recognized for excellence in generating data monetization outcomes and for

having great data monetization capabilities.10 It did not, however, begin its

journey with its capabilities in an advanced state. In 2011, the 154-year-old

financial services group was challenged with legacy technology that was

costly and slow, employees with outdated data science skills, and heavy

regulatory constraints regarding data use. Yet over time, because its leaders

took a long-term perspective on capability building, the company

established advanced enterprise data monetization capabilities that support

all kinds of data monetization initiatives across its global presence.

Phase 1: Selling Initiatives

In 2011, BBVA leaders were curious about the viability of selling

anonymized bank card data to generate new revenues. They sent a small

team to the MIT Senseable City Lab to come up with information solutions

that some market would want to buy. Leaders gave the team five million

anonymized bank card records, which the team prepared for analysis. This

meant adopting data management practices to clean up the data and to

define fields. It also meant learning how to confirm that records couldn’t be

reidentified and establishing parameters regarding where and how they

could sell the data legally.

At the time, the use of cloud computing at BBVA was banned by

regulators. But while the innovation team was at MIT, it was free to use and

learn about cloud software and services, so it did. It also learned how to use

algorithms and visualization techniques that were far more advanced than

anything that existed within the bank. The team’s MIT experience made it

realize that collaboration with others outside the bank could be very

worthwhile. Learning how to collaborate with entities like start-ups,

government agencies, and philanthropic organizations turned out to be key

to developing meaningful prototypes, which the team then used to

understand what kind of customers would be interested in bank card

information solutions and how much those customers would be willing to

pay for them.

After working with MIT for four years, the BBVA team had successfully

completed several selling initiatives, validating that customers would pay

for carefully analyzed bank card data assets. Also, it successfully

established an initial set of new practices and capabilities that BBVA could

leverage for future initiatives that involved selling bank card data offerings.

It had learned how to identify promising markets that could benefit from

economic impact analyses, such as urban planning and government

agencies.

This effort convinced BBVA leaders that selling data was a viable

strategy. At that point, the leaders set up a legally separate wholly owned

subsidiary called BBVA Data & Analytics (D&A). The new entity was

small, at first only four people, and was expected to become self-funding.

D&A would do this by operating as a separate information business that

sold information solutions based on the bank card data assets that had been

produced during the years of innovating with MIT.

To reinforce D&A’s autonomy, BBVA located the group in a building in

Madrid separate from the bank. The new physical space was designed to

include contemporary features that inspired collaboration and innovation

(e.g., movable furniture, glass wall whiteboards). The physical separation

from the incumbent bank helped preserve and nurture practices and lessons

gained during the MIT experiences so that new capabilities could thrive.

As a separate information business, BBVA D&A could adopt practices

they deemed worthwhile and build advanced enterprise capabilities required

to sell bank card information solutions. It’s important to note, though, that

the unit was operating at a tiny scale. They offered a small set of solutions

to some narrowly targeted markets, and the resulting revenues were

minuscule compared to the revenues of the incumbent bank. D&A’s small-

scale adoption of advanced practices translated into incredibly advanced

capabilities but for only a small set of data assets.

Phase 2: Improving Initiatives

As the BBVA D&A data scientists engaged in selling initiatives, they also

networked with data colleagues in the larger parent bank over coffee and

informal lunches. The subsidiary’s data scientists began to realize that

BBVA’s internal data efforts could be more fruitful with the more advanced

technology and practices they had adopted. As a result, they offered to help

a few of their bank colleagues approach their initiatives differently. In one

case, BBVA D&A used its more sophisticated data science capability to

help an initiative more effectively optimize the placement of bank branch

locations. The collaboration resulted in US$35 million in cost savings.

BBVA executives were thrilled to learn that data could create so much

value by improving the bank’s operations, but they realized that the bank

needed capabilities supporting different data assets, not the ones the

subsidiary had to offer. In effect, the bank needed capabilities like the

subsidiary but in service to other kinds of data. For example, bank functions

needed data assets for credit risk, customers, website activity, and more, not

just bank card transactions.

To generate more data assets that they could exploit, data scientists from

the subsidiary began advising internal bank initiatives and teaching them

how to use data science tools and techniques. The subsidiary still had to

fund itself, so it hired a financial expert to make sure every initiative it

advised had a stated economic goal and a way to measure its level of

achievement. If returns were positive, the subsidiary pocketed some of the

returns, per its consulting arrangement with the bank. The subsidiary also

absorbed 10–20 percent of the initiative’s costs under the condition that the

initiative contribute to the build out of the bank’s enterprise capabilities. So,

improvement initiative by improvement initiative, the subsidiary helped the

bank accumulate data assets and algorithms, and they pitched the new data

assets and algorithms to new initiatives to encourage reuse.

BBVA’s IT group agreed to support the centralized platform over time,

adopting more modern practices for its operation and its oversight. By

2017, the subsidiary had helped the bank deploy more than 40 improving

initiatives for 27 different business units. As a part of those initiatives, the

subsidiary helped unlock 34 new data assets by migrating 188 associated

data tables to a cloud-based enterprise platform that future initiatives could

leverage (thanks to a new cloud policy). Notably, BBVA D&A tracked

capability building as a part of its performance management process. For

example, it tracked how many new assets areas the D&A collaborations

helped to unlock for BBVA as well as other metrics like the number of

reusable machine learning models and the number of BBVA data miners

who had been upskilled to become data scientists.

Phase 3: Wrapping Initiatives

The BBVA D&A data scientists next realized that they could contribute to

the bank’s digital presence by using some of their algorithms and ways of

working to create appealing features for several of the bank’s consumer-

banking products. It was a hard sell, at first, to convince BBVA leaders to

invest in using data science to enhance the customer experience because the

business case was not like that for an improving or selling initiative. So, the

D&A team offered to pilot a single feature, a spend categorizer that would

use analytics to organize a customer’s transactions and then present the

results in a pie chart. It helped customers understand how they spent their

money. Unfortunately, the initiative took longer than expected. For one

thing, the customer data, a data set they had not previously dealt with,

needed to be cleaned up and made perfectly accurate. To put it another way,

it needed to be turned into a data asset. Also, the D&A team had to learn

how to build and train from scratch an AI model that could categorize

customer transactions.

The spend categorization had to be easily understood by BBVA’s

customers; otherwise the feature could do more harm than good. So, the

initiative team learned how to experiment with A/B testing to establish a

mechanism for understanding, over time, how well the feature was meeting

customer needs. Soon after BBVA launched the categorizer, it became one

of the most popular parts of the bank’s digital experience, second only to

money transfers. The resulting feature helped earn BBVA recognition for

best mobile banking in 2017, and the bank won that award for many years

after.

Building Capabilities Means Constantly Adopting New

Practices

When BBVA transitioned from selling to improving, it discovered that its

enterprise data monetization capabilities fell short. Its data assets were

insufficient, the company’s platform could not handle broad internal access,

and local data science skills were outdated. This shortfall happened again

when BBVA introduced wrapping; employees, it turned out, didn’t truly

understand customers because they were using outdated perspectives and

techniques. To some extent, these shortfalls occur because improving,

wrapping, and selling data monetization approaches rely on different

capability profiles.11 Improving, wrapping, and selling each place unique

demands on an organization. For example, organizations focused on

improving business processes will need internally focused capabilities, like

a searchable catalog of shared data terms and definitions for employees to

use to find data assets that can be used for analyses about operations.

Organizations focused on wrapping offerings or serving new customers in

totally new ways will need to focus on governance policies and processes

regarding how employees can and should use customer data assets.

Chapters 3, 4, and 5 will explore the capability profiles for improving,

wrapping, and selling in detail.

Time to Reflect

Here are the key points from this chapter to keep in mind:

• Capabilities are built by adopting practices. Consider your

organization’s weakest capability: What practices does your

organization need to adopt?

• Capabilities are often built to serve the needs of a particular

initiative. Consider your organization’s most robust capability:

How was it built or acquired? What practices contributed the

most to its current state? (If you don’t know, is there someone in

your organization who could tell you?)

• Enterprise capabilities are more valuable than pockets of

capabilities all over the organization. Which of your capabilities is

the most “enterprise,” that is, widely shared? How did it become

an enterprise capability? What tactics have been used to prevent

initiative teams from building isolated capability silos that are of

limited, local value?

• Capabilities only create value if they are put to use. What policies,

habits, or norms do you have in place that will ensure that the

initiatives that are underway find and use capabilities?

• Capabilities produce data assets that are accurate, available,

combinable, relevant, and secure. Data assets with these

characteristics are easily reused, and reusable data assets lead to

faster and cheaper data monetization initiatives. Does your

organization make a distinction between data and data assets?

The nice thing about getting a handle on your capabilities is that you can

spend more time thinking about ways to exploit them! That’s coming next.

In the upcoming chapters, you will learn how you can become great at

improving, wrapping, and selling.

Improving with Data

We will gain great efficiencies in the way we operate by having data and technology

help make faster and more informed decisions.

—Robert Phillips, CarMax

Your organization likely has been improving with data for decades. But

even if it’s commonplace for you, you might not be going about it

systematically or strategically. An organization with a state-of-the-art

improving approach can be recognized by (1) its vision for improving, (2)

the amount of value it creates and realizes, (3) the state of its capabilities,

and (4) who is held responsible for improving initiatives.

Does your organization have a specific vision for improving with data?

Do leaders encourage employees to be “data driven” and stop there? Or,

instead, do they encourage the use of data “to move from worst to first” (in

some respected industry-specific ranking), “make every employee one

hundred times faster,” or “eliminate one hundred million hours of wasted

customer time”—or some other compelling goal? Imagine how much easier

it is for employees to use data productively when they know what to aim for

and what will be rewarded!

How well are you creating and realizing value from improving

initiatives? With improving, the value-creation process is under the

organization’s control (unlike wrapping and selling, where customers are

involved). This means the organization can—at least in theory—directly

and actively manage the organizational adjustments needed to ensure that

value is both created and realized.

How strong are your data monetization capabilities? Do analysts take

personal pride in handcrafting byzantine spreadsheets and pivot tables? Do

data miners use software introduced in the age of data warehousing (or

earlier!)? Is data stored hither, thither, and yon? If that feels familiar, it

likely means that your organization needs to update its data monetization

capability-building practices. As you read in the last chapter, any data

monetization approach—including improving—benefits from better

enterprise data monetization capabilities. Better capabilities, better

outcomes.

Who is responsible for your improving initiatives? Is it the “IT

department”? This chapter will persuade you that the responsibility for

improving initiatives, the value they create, the value you realize from

them, and the capabilities you build for them must be shared among a much

larger crowd of stakeholders.

Questions to ask yourself

As you read this chapter, think about an expensive, inefficient, or

ineffective work practice at your organization. Maybe it’s hard to

onboard a customer or respond to supply chain glitches. Can you fix that

problem using data? Do you know how you would measure and monitor

the value created and realized from improving that work practice?

Research to consider

Among MIT CISR survey respondents, improving accounted for 51

percent of their financial returns from data monetization initiatives,

making it the most prevalent of the three approaches in the improve-

wrap-sell framework.1 This likely reflects the maturity of improving in

the marketplace.

Types of Improvements

Most organizations invest in improving initiatives. In a 2019 study of AI

initiatives,2 forty of the fifty-two initiatives studied targeted improving a

process or a task,3 ranging from predicting equipment failure to forecasting

passenger demand to recognizing abnormalities in lab images. For example,

GE created an AI-based contractor assessment for its three thousand

Environment, Health, and Safety professionals that assessed whether a

contractor satisfied GE’s safety criteria. This improvement saved hours of

time during contractor onboarding. (To give you a sense of the scale of this

improvement, at the time of the research, GE was hiring approximately

eighty thousand contractors annually.)

All improvements focus on enabling one of the three steps of the value-

creation process that you read about in chapter 1: they offer data (data

improvements), offer insight (insight improvements), or take some action

that creates value (action improvements). The names indicate the scope of

the improvement. As figure 3.1 illustrates, a data improvement, which just

provides data, relies on the recipient to grasp the significance of the data,

take some action based on that understanding, and create value. An insight

improvement offers task guidance, but the recipient must then act based on

the guidance to create value. With action improvements, the organization is

sure to create the value it set out to produce because the action is taken (or

nearly so) by the initiative.

Improving by Offering Data

Many improving initiatives provide more accurate, timelier, or better

integrated data to a user who previously did not have access to that data or

spent a great deal of time cobbling data together from various sources into a

spreadsheet. One of the supposed benefits of business intelligence reporting

initiatives was that they delivered much better data up and down the

organization. But was that data used productively? Did users know what to

do with it? In a few cases, yes; in many cases, no.

Figure 3.1

How improvements differ in their scope

Sometimes an improving initiative does deliver high-quality data to

decision makers who know what to do with it and are motivated to act.

Many will remember that the US Securities and Exchange Commission

(SEC) failed to detect the Bernie Madoff Ponzi scheme for so long in part

because the numerous tips that citizens submitted went to so many different

people that no one recognized a pattern of concerns.4 To protect against

similar mistakes in the future, the SEC created a single data repository

(TCR) that consolidated tips, complaints, and referrals. This far superior

data source was put into the hands of analysts capable of using it to identify

potential violations of securities laws. Once the analysts surfaced a possible

violation, they were responsible for setting the wheels in motion for further

investigation, which resulted in either TCR resolution or enforcement

action (i.e., creating value from the TCR data). The key was getting the

right data into the right hands.

But more often, an organization has to do much more than deliver high-

quality data to smart decision makers. Organizations also must actively

ensure people can and are willing to engage with that data. Users cannot

engage when they are poorly trained. (Data literacy programs and analytics

training are great ways to address such skill hurdles.) Also, users will not

engage when they are distrustful or too busy. If you choose just to offer

data, make sure you monitor that data’s use as well as the steps between

use, value creation, and value realization.

Improving by Offering Insight

Improving initiatives can offer insights in the form of benchmark scores,

exception reports, advice, and different kinds of visualizations and alerts.

While offering insight in no way guarantees the use of insight, it is at least

one step closer to value creation.

At contemporary apparel retailer GUESS, the data science team won over

the company’s creative staff (clothing buyers and designers who had little

time or inclination to embrace insights based on data) by giving them cool

devices, hiring a graphic artist to develop a fun, contemporary app

experience, and creating visual dashboards that included photos of apparel

and store layouts.5 As a result, the buyers and designers began using

insights about top-selling fashions, regional demand, and effective

merchandising. They saved time previously wasted in making sense of

tabular reports with cryptic SKU numbers because everybody was “already

on the same page” regarding important product sales trends. Based on the

accessible and consumable insights, they focused their time on developing

and deploying new selling, demand management, and merchandising

strategies (i.e., taking actions that created value).

As a rule, insights are most likely to lead to action taking and value

creation if they are delivered to people responsible for acting. Most of the

forty AI initiatives mentioned earlier delivered insights to experts of some

sort. For example, equipment failure predictions went to individuals

responsible for taking equipment offline, not to a machine operator;

passenger demand forecasts went to people responsible for changing flight

schedules, not to caterers; alerts about abnormalities in lab images went to

radiologists, not nurses. Insights must be delivered to people (or systems)

who have the authority and ability to act on them.

Improving by Triggering or Prompting Action

As you might suspect by now, you can avoid the risk of a breakdown in the

value-creation process by triggering or prompting action. Many AI-based

improving initiatives trigger the automatic execution of some tasks. In the

study mentioned earlier, one-third of the AI improvements involved

automation. They included action taking like automatically remediating

network security breaks, automatically reordering items that were low in

stock, adjusting equipment settings after changes in the operating

environment, and automatically sending an email containing ideal

marketing content to a customer.

Full automation is not always easy to achieve because so many

complementary organizational adjustments need to happen. Consider the

case of Trinity Health, one of the largest healthcare delivery systems in the

US.6 An upcoming remodel for a flagship hospital provided an opportunity

to pilot and execute IoT-enabled use cases, including one that helped nurses

respond faster to patients and reduce patient fall risk. The improving

initiative automatically sent an alert to a nurse’s mobile device if a patient

with a high risk of falling started to get out of a sensor-laden bed so the

nurse could respond faster. Before the alert could be automated, the team

had to do many things: establish business rules that spelled out exactly who

to alert, in what order, and under what conditions; clean up the patient data

and create accurate fall risk scores; redesign the process by which nurses

made rounds; educate staff to follow the new processes and procedures; and

create incentives that would get staff to buy into the new policies and

procedures. After the initiative was deployed, Trinity leaders viewed this

use case as a success. They created value in the form of a 57 percent

reduction in nurse-call response time, which correlated with fewer patient

falls.

In some initiatives, triggering or prompting action does not mean

completely automating it but just making human action simple and

straightforward. For GE’s AI-based contractor evaluations, for example, a

professional pressed a button to initiate evaluation assistance. The AI model

then analyzed the document and reported on whether the GE safety criteria

were satisfied or not. The application offered easy access to the rationale

underlying the AI assessment, which allowed the professional to quickly

accept most of the assessments and move on, creating significant

efficiencies in the process.

Creating Value from Improving

When it comes to creating value from improving, initiative teams must first

articulate the type and magnitude of value that the initiative intends to

create. Then, they must make any complementary organizational changes

that are needed to ensure that value gets created.

In a sea of possible improving outcomes, organizations must clarify up

front what kind of value they most want to generate from an initiative. At

times, initiative teams aren’t sure if the type and magnitude of desired value

is possible. In these cases, they draw heavily on pilot testing and

experimentation. They can zero in on an initiative’s value potential by

investigating an improvement at a small scope or in a controlled manner.

Pilot tests allow the organization to establish value baselines (e.g., the pre-

improvement level of productivity of a process). Experiments often require

developing a measurement approach for tracking value creation that,

fortunately, can be sustained after deployment and over time.

For example, leaders at Trinity Health also wanted to know up front what

kinds of value they could create from improving initiatives (like the nurse

response initiative) in a “smart” hospital room. Specifically, they wanted to

improve the quality and efficiency of patient care. But in what ways? The

leaders asked a pilot team to fit thirty patient rooms with sensors

everywhere: in medical devices, beds, patient wearables, and strategic

locations like doorways and handwashing stations. In one pilot, the team

tested whether monitoring hand hygiene would lead to better infection

control outcomes (improving by offering data). Data from handwashing

sensors and staff-location sensors was used to monitor handwashing as care

providers entered a hospital room. Analysts then calculated handwashing

percentages and correlated those with hospital-borne infection rates.

These results were shared with managers who were responsible for staff

handwashing practices. The pilot test results justified broader deployment

of the hand hygiene initiative and helped the initiative team establish

realistic value-creation goals. Three years after the pilot, Trinity Health had

recorded over 14.5 million hand washes, representing increased adherence

to hand hygiene procedures within medical-surgical and critical care patient

areas. This adherence had led to a 29.7 percent reduction in C. difficile

infections and a 24.5 percent reduction in MRSA infections.

Regardless of whether an organization deploys an improving initiative

that offers data or insight or triggers or prompts action, the complete data-

insight-action value-creation process must happen for value to materialize.

The top risk to value creation with the improving approach is not making it

to action. One way to mitigate this risk is to extend the initiative’s scope (as

in figure 3.1). For example, once the SEC had its TCR database in place, it

could streamline the work of analysts by providing them with actionable

insights. That is, the initiative team would increase the scope of the

improvement from offering data to offering insight. And at GE, until their

contractor evaluation application served up easily accessed, clear

explanations for the AI model’s assessment, evaluators were more likely to

ignore it and instead perform slow, manual evaluations. The GE initiative

team increased the scope of the improvement from offering insights to

prompting action.

Because so many inaction-risk challenges can pop up, it is important to

be keenly aware of how the value-creation process is expected to unfold. If

possible, the process should be monitored by either instrumenting data or

insights to see if they are used or by asking users directly about use,

periodically. Are there obstacles related to the ability of decision makers to

use data that could be resolved with training or assistance? If insights are

not being acted on, perhaps the insight is going to the wrong person, not

someone with the authority and ability to act on it. As you saw with some

of the improving examples, to make sure that an improving initiative

creates value, it might be necessary to change related policies or business

rules, redesign processes, change how data is collected, redesign jobs,

change performance measures or incentives, and reskill or replace people.

Realizing Value from Improving

You already know from chapter 1 that organizations must stay on top of

value realization from any initiative. And you know that improving

initiatives often seek to standardize or simplify processes and work tasks,

creating value in the form of efficiencies. For the organization to realize

value from efficiencies, the resulting slack must be redirected or removed.

That is, if the efficiencies reduce the need for headcount, the equivalent

headcount must be removed from the process or redirected to other work.

The savings flow to the bottom line. At GE, for example, the AI

improvement initiative reduced the time GE professionals spent reviewing

documents in the office, freeing them up to focus on higher-value work, like

getting into the field to look for and solve safety problems. However, keep

in mind that some slack is a good thing. Slack allows for innovation and

also helps organizations deal with environmental uncertainties, such as

sudden increases in demand.7

What was not mentioned in chapter 1 is that an improving initiative can

also create value by making a process more productive or by improving

product quality. The value created by producing more and better products is

realized—turned into money—by selling them. Or, as in the case of GUESS

and the insights for clothing buyers and designers, the value created in the

form of better product placement was realized in the form of increased

revenues from fewer discounts and markdowns. In other cases, realizing

value from better products might require price increases. In chapter 4, you

will read more about realizing value from product sales, since that is the

dominant value-realization mechanism for wrapping initiatives.

Figure 3.2

Value realization from improving initiatives

To summarize, as shown in figure 3.2, some of the value created by the

improving initiative is realized by the organization as reduced costs, and

some is realized by the organization as increased revenues. And some value

is left on the tree, so to speak—some value has been created that is not

turned into money. It might take the form of slack capacity to be used for

innovation, it might reduce pressure on employees or managers, or it might

go home with customers. The amount of value left on the tree could be

considerable if slack is not removed or redirected.

Improving at Microsoft

Microsoft, one of the world’s most recognizable technology companies,

exemplifies a company that invested big in improving, with initiatives of all

shapes and sizes. Though headquartered in the US, in 2022, the company

operated globally, employing more than 163,000 people. Despite ongoing

economic shocks and changes in consumer behavior, by June 2022,

Microsoft had grown to a market capitalization value exceeding US$2

trillion.8

In 2014, the company faced increasing competition from the likes of

Google, Apple, and Oracle as well as significant shifts in consumer

behavior and expectations. In particular, the software industry’s movement

to cloud-based services called for a substantial change to Microsoft’s

business model: offering cloud services required constant information about

service usage, a deep understanding of customer attitudes and needs, and

entirely new pricing and selling strategies.

Incoming CEO Satya Nadella embraced this challenge in 2014 with

outstanding results. Just three years later, cloud revenues had grown by 93

percent, and Microsoft’s share price had more than doubled. Also, 61

percent of Microsoft employees were using data and analytics monthly.

Nadella’s clear vision, combined with the continued prioritization of

evidence-based decision-making, resulted in a cascade of internal

improvements that transformed Microsoft into a data-fueled organization.

Microsoft engaged in improving with an explicit vision. Like many

CEOs, Satya Nadella used the term “data-driven” a lot as he talked with

audiences both inside and outside the company. But it was obvious what

Nadella meant by the term. He meant he wanted Microsoft employees

everywhere, around the globe, to use data to change the very nature of their

work so that the company could successfully transition from a software

product company into a cloud-based services provider.

To back up his words, Nadella made bold moves. He consolidated core

business functions (e.g., sales, marketing), breaking down product-oriented

silos. He adjusted employee incentives so that one of the three core pillars

of assessing an employee’s performance was how well they collaborated

across the organization. And he set a goal that every Microsoft employee

would use Power BI to perform work. Collectively, these efforts created an

environment in which leaders could create value from data. As a result,

improving initiatives sprang up across Microsoft.

Let’s look at one example of an improving initiative championed by

leaders in finance (specifically one that offered insights). The leaders

wished to raise the effectiveness of Microsoft’s financial analysts by

reducing the cycle time between financial analysis and field action. The

goal of the initiative was for analysts to spend less time analyzing financial

data and more time communicating insights to their partners in sales. The

finance team adopted a suite of comprehensive, flexible analytical tools so

that analysts could answer a wide range of questions on the fly during

business reviews and answer impromptu questions from sales personnel. To

hone the analysts’ communication skills and presentation techniques,

leaders established a storytelling training program for them that included

webinars, live demonstrations, videos, and in-person sessions. Within

fifteen months, these efforts helped financial analysts reduce their time to

produce insights by 30 percent and to redirect that time to sales partner

communication. These outcomes aligned with Microsoft’s need to create

entirely new pricing and selling strategies as it shifted to its cloud services

business model.

This short example demonstrates some key points about the improving

approach to data monetization:

• Nadella’s vision to shift to cloud services demanded significant

efficiencies that would allow effort to be redirected toward sales.

• The example initiative made Microsoft’s existing financial analysts

more efficient by decreasing the time required to produce

financial analyses.

• Analysts were also made more effective in that they could deliver

more meaningful insights and immediately respond to their sales

partners’ queries.

• Finance leaders (i.e., the owners of the business process being

improved) were accountable for ensuring value creation and value

realization.

• To support the value-creation process (i.e., increase the likelihood

that salespeople would act on the analysts’ insights), finance

leaders established a storytelling training program that taught

analysts how to present actionable insights in a compelling

manner.

• Finance leaders measured success (i.e., value creation) as the

reduction in data-gathering time and corresponding increases in

time devoted to sales partner communication.

• Value realization occurred when the time saved by analysts was

redirected to the more valuable activity of supporting their sales

partners who were growing sales.

Again, this is just one example of improving at Microsoft after Nadella

came on board as CEO. There were many other new improving initiatives

throughout the company.

Data Monetization Capabilities for Improving

Chapter 2 explained that five data monetization capabilities, namely data

management, data platform, data science, customer understanding, and

acceptable data use, power data monetization initiatives. Like any kind of

initiative, improving initiatives with access to more advanced capabilities

are associated with greater data monetization returns.9

If you are curious to know how advanced capabilities need to be,

research indicates that organizations classified as top performers in

improving (those that reported top scores in improving outcomes) have the

distinct pattern of capability practices illustrated by figure 3.3.10 (As you

will see in the next two chapters, top-performing wrapping and selling

organizations have their own distinctive patterns.)

Figure 3.3

The capabilities of top-performing improving organizations

While the organizations identified as top performers in improving have

better capabilities than bottom performers (whose fans would be blank),

they do not necessarily have advanced data monetization capabilities. They

do, however, possess the following capabilities, which foster their ability to

achieve their improvement goals:

• These organizations have accurate master data, particularly about

operations, such as the chart of accounts, product or part numbers,

employee identifiers, location codes, or asset identifiers.

• They provide internal access to data and tools through a data

platform built using cloud and advanced technology, making data

access fast, ubiquitous, and cost efficient.

• Their data science capabilities are well established at the statistics

level, allowing them to provide insights necessary to optimize

processes and tasks.

• A basic level of customer understanding is undoubtedly important

for improving initiatives so that changes are made in line with

customer needs. However, on average, top-performing improving

organizations do not have a notable level of this capability. This is

probably because many improving initiatives are not customer

facing.

• Internal use of sensitive proprietary data is formally monitored and

governed to ensure appropriate data use.

In sum, top-performing improvers draw on capabilities that help them

produce data assets that can be reused in ways important for them.

Data Monetization Capabilities at Microsoft

People often assume that technology companies are great with data. But just

like any organization, technology companies can establish capabilities

locally instead of establishing data monetization capabilities at the

enterprise level. They can also become comfortable with established ways

of working with data—using spreadsheets and SQL queries—at the expense

of introducing new tools and training.

This was certainly the case at Microsoft before Nadella’s appointment.

However, as Microsoft leaders and employees came to rely more on data,

chief information officer (CIO) Jim DuBois established four corporate

shared services groups to amass enterprise data monetization capabilities.

These corporate groups worked closely with leaders across the organization

to establish practices needed by new data monetization initiatives.

To illustrate the interplay between capabilities and initiatives, consider

another improving initiative, one that streamlined Microsoft’s sales

processes. Sales leaders set out to increase the time salespeople spent with

customers by 30 percent, or 1.5 more days per week. This goal was again

driven by Microsoft’s business model shift, which required salespeople to

understand customer attitudes and needs more deeply.

Microsoft initially lacked the necessary data management capability for

this initiative; critical data was buried in siloed, product-line applications,

with inconsistent data definitions and coding conventions. Salespeople drew

on data from more than eighty different systems. They had to contend with

multiple definitions of the term “sales lead,” forcing them to waste time

manually translating and integrating the information they needed. As a first

step to a better data management capability, sales leaders adopted a process

for standardizing shared sales concepts—such as what is meant by

“pipeline” or “lead”—so they could build consensus on the agreed-upon

definitions among salespeople, sales managers, and IT.

To provide salespeople around the globe with access to the new and

improved sales data, Microsoft had to modernize its data platform

capability. The company invested a year in developing the Microsoft Sales

Experience platform using Microsoft Azure cloud technology. The platform

team identified the key source systems from which to pull data and

established data movement processes; standardized fields so that sources

using different field identifiers and formats could be integrated; and

established reference data (such as a standard list of country codes) to

maintain consistency in the values of commonly used fields. The resulting

platform ingested and consolidated sales data to produce 360-degree views

of Microsoft’s relationships with corporate customers. For each customer,

this new system summarized purchases, issues and complaints, and

previous communications.

The platform hosted a collection of dashboards driven by Power BI

(Microsoft’s own business analytics service) and other data services.

Process designers arranged these dashboards into workflows to support

different sales personas, such as sellers and sales managers. Each unique

workflow offered users helpful access to the company’s data science

capability; the salespeople accessed information and actionable insights that

were specific to their accounts and work tasks.

Because this initiative involved improving key customer-facing

processes, a customer understanding capability was needed. (The earlier

Microsoft example of improving the efficiency of financial analysts did not

depend on a customer understanding capability.) They adopted a practice of

drawing on the collective knowledge of the sales force, a foundational

practice called sensemaking, or listening to customers and making sense of

their needs. Salespeople provided input into workflow ideas, reporting

requirements, and even features that could be used to feed sales-related

machine learning models (e.g., a model to predict the likelihood of a deal

closing). Their input, which in effect was infused into new work tools and

tasks, helped Microsoft build a customer understanding capability.

Finally, Microsoft developed its acceptable data use capability by

monitoring dashboard usage and being transparent about this oversight. The

company remedied barriers to data access and curbed inappropriate use by

providing better support, training, and incentives.

Microsoft’s data monetization capabilities turned Microsoft’s sales data

into data assets reused by the reengineered enterprise sales process (an

improving initiative that prompted salesperson action). To track value

creation, sales leaders monitored their employees’ use of dashboards and

measured the decrease in time spent on administrative duties. To realize

value, leaders encouraged salespeople to reallocate their time (on average,

1.5 days per week) to customer-facing activities. In effect, from this

improving initiative, sales leaders realized value equivalent to avoiding the

cost of increasing the size of their trained and experienced salesforce by 30

percent.

Ownership of Improving Initiatives

The ideal leader for an improving initiative is the owner of the process,

activity, or task being improved; for simplicity, let’s refer to improving

initiative owners as process owners. A person in this role is accountable to

the organization’s leaders for a process or task outcome that influences the

organization’s bottom line (e.g., the cost of making something, the speed at

which something is made, and the quality at which something is delivered).

She understands what makes her process tick, how process tasks are

accomplished, and what information is relevant to the work being done. The

process owner also understands how the performance of her process relates

to or impacts the organization’s key performance goals.

At Microsoft, the owner of the sales improvement initiative was the head

of Microsoft’s enterprise sales business unit (not someone in IT, on the

process design team, or leading data). Only this person was in a position to

ensure that the desired value was created (an average reduction of 1.5 hours

per week of administrative work) and to manage the risk of inaction. The

head of sales drew on his power, clout, and control over resources to keep

the initiative moving along. And he was perfectly positioned to redirect any

slack created as a result of the initiative to other sales unit needs, as he did.

There are cases in which process owners need help from other leaders to

realize value from productivity or product quality gains or to realize value

from efficiency gains that arise in downstream processes because of

improvement to the processes they control. For example, the owner of

Microsoft’s financial analyst improvement initiative, the person in charge of

financial analysts, had to work with another leader—who had oversight of

field sales—to make sure that financial analysts’ efficiencies ultimately

translated into their sales partners’ generating additional sales.

As you might sense, process owners—and other leaders on whom they

rely—are vital for improving initiatives. But it takes a village for

improvements to succeed. For example, at Microsoft, the entire global

workforce was expected to engage in new ways of work and use data

whenever possible for tasks of any kind. At Trinity Health, smart hospital

room efforts unfolded with the help of IT people, data analysts, clinicians,

and all levels of hospital staff. At GUESS, improvements to selling, demand

management, and merchandising evolved through collaboration among app

programmers, graphics designers, data teams, store employees, operations

teams, and increasingly engaged buyers and designers. In fact, in the

improving examples throughout the chapter, myriad people from all levels

across the organization have been responsible for some facet of data

monetization. Improving, as it turns out, is everybody’s business.

Time to Reflect

Regardless of their size, organizations that wish to explore improving

initiatives should begin by considering their vision for improving, the value

they intend to create and realize, their capabilities, and whom they will

involve in improving initiatives. Like Microsoft’s Nadella, leaders must be

clear about how data assets will be used to improve operations and generate

value. Organizations must also understand what data monetization

capabilities are required to support their improvement goals and how they

will be developed. Finally, for any improving initiative, leaders must

designate a process owner with accountability for the initiative’s success,

who will be expected to share responsibility broadly among the many

people who need to be engaged.

Here are the key points from this chapter to keep in mind:

• Improving initiatives deliver (1) data or (2) insight to decision

makers, or they prompt or trigger (3) action. Which kind of

improving initiative (data, insight, or action) is easiest for your

organization?

• Improving initiatives don’t create value until some action takes

place. For your improving initiatives that deliver data or insight,

how well is your organization tracking action and value creation?

• It is crucial that value created be realized and reflected in your

bottom line. Does your organization realize value from its

improving initiatives? Or is it leaving money on the table?

• Improving initiatives draw on all five capabilities, not just data

management. Looking back, can you think of an initiative that

failed because the capabilities it needed were not available?

What capability-fostering practices did that initiative require?

• Improving initiatives should be owned by people who can ensure

that value is created. Looking back, can you think of an improving

initiative that did not create value because the wrong person

owned it? Who should have owned that initiative?

Ultimately, improving is a great place to start if your organization is just

beginning its data monetization journey. For organizations that have

mastered improving, the subject of the next chapter—wrapping—is a

natural next step.

Wrapping with Data

Companies that create enhanced customer experiences using proprietary data will limit

the threat of substitute products and thereby create sustainable margins.

—Gregg Jankowski, AlixPartners

The last chapter explained how you can use data to improve work practices

so that your organization produces more or better output at less cost. As you

were reading chapter 3, you were probably focusing inward, thinking about

how your organization functions today. Now it’s time to turn your attention

outward, focusing on enhancing what your organization produces as

perceived by your customers or constituents. Data wrapping is all about

your customer or whomever your organization serves.

When you use data to create a feature or experience for a product with

the implicit objective of delighting the customer, you’ve created a wrap.

You aren’t generating a standalone information solution; you’re enhancing

the value of an underlying offering. The product can be physical (a tractor),

intangible (a bank account), service based (a taxi ride), noncommercial (tax

servicing), or for profit (freight delivery). All of these products can be

wrapped. For example, a tractor can be wrapped with a digital display that

shows operational performance; a bank account can be wrapped with a

chart that categorizes the account owner’s spending; a taxi trip can be

wrapped with a fare estimator; a tax form can be wrapped with

prepopulated fields; and freight delivery can be wrapped with notifications

of expected delivery times. These features and experiences offer exciting

possibilities if your organization struggles with product commoditization

and rising customer expectations. In a competitive environment, wrapping

can help your offerings stand out in the marketplace.

Perhaps your organization feels pressure to give customers and

stakeholders more. You might have efforts underway to map out customer

journeys, find and fulfill unmet customer needs, or engage in customer

cocreation. If you do, it’s time to consider data wrapping and tap into a

world of data-fueled ways to change or freshen up your offerings.

Questions to ask yourself

As you read this chapter, reflect on the friction your customers—or

constituents—experience with your current offerings. How can you use

data to make your products more useful, easier, or more fun to

experience? Can your offerings do more to help your customer save

money, make money, or achieve a goal that matters to them?

Research to consider

In 2018, of five-hundred-plus product owners surveyed, 85 percent had

wrapping initiatives underway, and 55 percent of those wraps were

already deployed in the marketplace.1

Types of Wraps

Organizations today must be able to walk in the shoes of their customers.

This is true whether they are working hard to sell goods or services, to

achieve a philanthropic mission, or to serve the needs of a constituency of

citizens. Delivering effective and enjoyable offerings is only possible with a

deep understanding of customer needs and the extent to which customers

perceive those offerings are meeting their needs.

By listening to their customers, organizations might learn that their

offerings are inconvenient to buy, cumbersome to use, or hard to return.

Wraps to the rescue! Wraps can help customers at any point along the

customer journey. Wraps that help a customer solve a problem related to the

offering make the offering more valuable to the customer. Most websites

and apps deliver wraps that enhance core offerings. Consider an app for a

meal kit service that helps a customer choose suitable meal options, manage

nutritional intake, and identify optimal recycling options. These

informational enhancements are wraps that help the customer better

acquire, use, and retire their meal kits.

Wrapping can add value to offerings in both business-to-consumer (B2C)

and business-to-business (B2B) settings. For example, chapter 2 recounted

how BBVA offered a spend categorizer to consumer-banking customers to

delight them and get them to use their bank cards more often. The wrap

used machine learning algorithms to sort customer transactions into rent,

food, and other budget categories and then displayed a customer’s spending

activities as a simple chart. The bank promoted the categorizer as a way for

BBVA consumer-banking customers to manage their financial health.

Later, BBVA also created wraps for business customers. The bank

offered store activity dashboards to merchants that purchased BBVA POS

services. This dashboard wrap drew on data collected, anonymized, and

aggregated from BBVA bank card transactions and POS terminals. It

displayed insights and alerts that answered common merchant questions

like, “How much total revenue is my business generating compared to the

average in my business sector?”

All wraps fall into one of the same three basic types that improvements

did. They offer data to the customer (data wraps), offer insight to the

customer (insight wraps), or take some action that benefits the customer

(action wraps). With wraps, the customer achieves a goal by taking an

action as a result of being presented with data, an insightful analysis, or an

action trigger. And by taking action, customers create value for themselves.

Whether and how the organization realizes value from the wrap is a topic to

be discussed later.

Figure 4.1

How wraps differ in their scope

The names for data, insight, and action wraps simply describe the scope

of the wrap in the value-creation process. A key difference in the three

types of wraps is the organization’s visibility into the customer’s value-

creation process. As figure 4.1 illustrates, the wrap that simply provides

data leaves insight finding and action taking to the customer. The

organization has little visibility into whether and how insight and action

happen and how much value is created for the customer. An insight wrap

points the customer in the right direction, but the customer must take the

action that creates value. With action wraps, customer value creation is all

but guaranteed because the wrap is designed to trigger action. Insight and

action wraps can be instrumented to give the organization some visibility

into use but not necessarily into value creation.

All of this should sound familiar to you—unless you skipped the last

chapter. The important twist in this chapter is that wrapping is intended to

help your customers achieve their goals, not to help you and your

colleagues achieve your goals.

Data Wraps

Data wraps offer customers data that can take many forms: simple reports,

dashboards, charts, and even data feeds that customers can integrate into

their own systems. For example, social media companies give advertisers

simple reports detailing how consumers are responding to their ads.

Advertisers can use this information to review the success of their ads and

withdraw, adjust, or expand their social media advertising buys. Some local

governments give citizens simple reports that reflect a household’s status

with the municipality. Citizens can log in to a portal to assess their

compliance with local obligations like dog licensing and their standing with

local services like garbage pickup.

Compared to the other types of wraps, data wraps do the least work for

the customer, so they have the weakest association with customer value

creation.2 That is, data wraps are the least likely to generate customer value.

However, one attraction of data wraps is that they require the least effort to

launch.

Insight Wraps

An insight wrap simplifies customer decision-making or problem-solving

with respect to the core offering. Wraps that offer insights provide

customers with the next steps to take, recommendations, flags for irregular

activities or unusual data patterns, benchmarks, or alerts.

A food and beverage provider offered a party planner chatbot to party

hosts to prevent over- or underspending on drink and snack purchases.3 The

chatbot used advanced analytics to analyze historical sales data and

generate optimized shopping lists for the hosts based on the type of party

and the number of attendees. Those party hosts who followed the

recommendations created value for themselves by throwing a less costly

party. It’s worth noting that not all party hosts will do this! As an insight

wrap, the chatbot relied on the host to follow through and make the

recommended purchase. Some hosts might have second-guessed the list,

restricted their purchases to items for which they had coupons, or ignored

the list entirely. For all those party hosts, the chatbot would not have lived

up to its value-creating potential.

Insight wraps get organizations one step closer to customer value creation

than data wraps; they point customers to possible solutions that could

generate value for them. However, for customer value to materialize, these

wraps need to deliver insights that customers can understand and act upon

to achieve their own goals. As a result, insight wraps require more

advanced data monetization capabilities than data wraps, particularly more

advanced data science skills and a deeper understanding of customer wants

and needs.

Action Wraps

If the food and beverage provider’s chatbot had ordered the items on the

shopping list for the party host, then it would be an action wrap. It would be

taking action on behalf of the party host. Imagine if the chatbot could detect

the party host’s current location, discover which stores in the area had the

shopping list items in stock, order them, and schedule the order for drive-

through pickup. Now that would be an action wrap!

Action wraps typically include analytics features that first determine

what change in the customer’s situation is needed. Then the wrap comes as

close as possible to making that change happen. There are some interesting

action wraps in IoT contexts. For example, one farm equipment provider

placed sensors on its equipment and collected data to monitor performance

(device status, temperature) after it was installed on the customer’s

premises. The equipment provider created an action wrap that predicted

potential equipment failure, ordered parts, and scheduled a service call on

behalf of the customer.

Sometimes action wraps require the customer to initiate the final action,

but they make it very easy to do so. BBVA created an app feature that

notified a customer about a refinancing opportunity that appeared to fit their

needs. It offered to connect the customer with a live financial advisor who

was teed up to support the customer with a refinancing option. The feature

laid the groundwork, but the customer made the final move with a click.

So, if action wraps are so great, why don’t organizations always create

wraps that act? Well, often, they can’t. They might not be confident what

action should be taken or be allowed to act for regulatory reasons. They

might also not have suitable systems or processes in place, or their

customers might not want them to act. For these reasons and more, data and

insight wraps might have to suffice. But regardless of the kind of wrap the

organization creates, it’s always important to keep the customer’s final

action (or actions) in mind. What will the customer do with the data or

insight provided in the wrap? What kind of value—and how much value—

will that action create for the customer? As you will see later, the amount of

value the customer creates from the wrap puts a ceiling on the amount of

money the organization can ultimately realize.

Characteristics of Great Wraps

Wraps that customers find useful and engaging are more likely to increase

unit sales, command higher prices, grow market baskets, and improve

customer retention.4 Useful and engaging wraps have four characteristics:

They anticipate, meaning the wrap understands the customer’s need in

advance. They adapt, meaning the wrap meets the customer’s need in a

tailored way. They advise, meaning the wrap supports evidence-based

decision-making. And they act, meaning the wrap performs an action that

benefits the customer. Figure 4.2 illustrates these four characteristics, called

“the four As.”

Let’s take BBVA’s spend-categorizer wrap as an example. In 2016,

BBVA was first to market with its pie chart of categorized expenses, so it

captivated customers with its novelty. But today, that original pie chart

would not get high marks on the four As. It would get low marks for

anticipate because it was inherently backward-looking; it simply showed

what a customer had already spent. The early pie chart was somewhat

adaptive in that it had a few tailoring features based on customer needs or

preferences. It would get low marks for advice because the pie chart didn’t

help the customer decide what to do about the information. The pie chart

would also get low marks for act; the pie chart certainly did not take any

action on behalf of the customer.

Figure 4.2

The four characteristics of useful and engaging wraps

Intuitively, an insight wrap should earn high scores on advising, and an

action wrap, of course, should get high scores for acting. The spend

categorizer is a classic example of a data wrap. Data wraps, by their nature,

primarily rely on features that adapt. They draw on customer-specific data

and generate information tailored to the customer at hand.

Higher scores on the four As will increase data monetization outcomes

for customers (in value creation) and organizations (in value realization), so

it’s only natural for organizations to evolve data wraps into insight wraps

and then into action wraps over time.

Anyone who looked at BBVA’s financial manager app five years after

launch would have appreciated how far its features had come since the early

pie chart days: BBVA customers could view predicted expenditures for the

upcoming two months so that bills or payments wouldn’t catch them off

guard; that would earn this wrap a high score for anticipate. Customers

could set spending targets, and the app would help them stay under the

targets by alerting them before they reached a cutoff level; that would earn a

high score for adapt. Customers could see, on average, what people in their

neighborhood—presumably, people like them—were spending on things

like utilities and food, which could lead them to rethink their spending; that

would earn a high score for advise. Finally, as described earlier. the app

could alert the customer to a new refinancing opportunity, connecting the

customer with a loan agent with the push of a button; that feature would

earn this wrap a high score for act.

In sum, the four As are a checklist that helps gauge a wrap’s potential for

creating customer value. Organizations can score a wrap on the four As to

determine how useful and engaging a wrap is likely to be. By comparing

the scores of various wrapping proposals, organizations can identify the

opportunities that are most likely to inspire action, create customer value,

and pay off for the organization.

Creating Value from Wrapping

Customers’ willingness to pay for an offering will increase when they

believe the wrap has made the offering more valuable—that is, it is easier

and more enjoyable to find, acquire, use, store, maintain, or retire the

offering. This is the metric to watch when wrapping: has an uplift in the

value proposition of the offering been created? Uplift can be monitored

using various techniques, like tracking customer usage, A/B testing,

conducting controlled experiments, or surveys. With an enhanced value

proposition, the organization can attract new customers or motivate existing

customers to pay more, spend more, or stay longer.

The Joint Sphere

Consider that when an organization delivers a wrap, it only creates potential

value for its customers. Ultimately, it is customers who create value for

themselves by taking some action at the end of a value-creation process; the

organization gains if and when it realizes a portion of this value, for

example, by charging a higher price or by retaining the customer’s business

despite competition. Therefore, customer value creation must be the

primary focus of organizations that pursue the wrapping approach.

By designing a wrap to give the customer either data, insight, or action,

the organization, in effect, chooses how and how much it will help customer

value materialize. Organizations that care deeply about creating customer

value want to work closely with customers in the design and development

of wraps. The area of joint involvement in wrap design and development is

called the joint sphere (see figure 4.3).5

Figure 4.3

Organization and customer working together to generate customer value

The size of the joint sphere (the overlapping ovals in figure 4.3)

represents the extent to which the organization and customer share

knowledge (and related resources) to achieve the customer’s goals. Without

any joint sphere, the organization is on its own to discover how to improve

the customer’s value proposition. And if the joint sphere is very small, the

organization will likely be restricted to offering a data wrap. Conversely,

when the joint sphere is large, the organization is more likely to have the

opportunity to offer an action wrap.

A larger joint sphere results in better outcomes for customers and the

organization alike. Customers are more likely to create value, and

organizations are more likely to be able to realize part of that value for

themselves. Organizations grow the joint sphere by building trust and

digital connections with their customers. Customers grow the joint sphere

by sharing their data and permitting organizations to take action on their

behalf.6 In B2B contexts, moving from transactional relationships to

customer partnerships enlarges the joint sphere.

How can an organization expand the size of the joint sphere it shares with

its customers? It can begin by discovering what customers are trying to

achieve with its offerings—and how well (or not) those goals are being met.

Initially, an organization can learn this information by asking customer-

facing employees and by experimenting with data wraps that draw on the

data it already has. Next, you will see how PepsiCo grew its joint sphere

and changed the nature of its relationships with its large retailer customers.

Creating Value from Wrapping at PepsiCo

PepsiCo owns some of the world’s biggest food and beverage brands,

including Pepsi-Cola, Lay’s, Gatorade, Tropicana, and Quaker. In 2021,

PepsiCo’s products were consumed upwards of one billion times per day in

more than two hundred countries and territories, generating revenues of

US$79 billion.7

Though the company had successfully established itself as a global leader

in convenience foods and beverages in the late twentieth century, around

2010, PepsiCo saw industry growth begin to slow as markets reached

maturity, competition increased, and its core consumer group aged. Rather

than continuing to increase product variety, PepsiCo turned to data as a

source of competitive advantage.8 Specifically, the company wanted to use

data to identify pockets of potential growth and understand when and where

to place specific products in a particular retail outlet, among other goals.

In 2015, PepsiCo established a new business unit, the Demand

Accelerator, that led the development of integrative, data-driven marketing

services for its large retailer customers. The Demand Accelerator helped

PepsiCo’s IT unit build data monetization capabilities, provided enterprise-

level analytics support, and supported new kinds of retailer collaborations.

Ultimately, PepsiCo’s collaborative approach to developing wraps helped

build relationships that created win-win-win initiatives for shoppers,

retailers, and the company itself. As a result, PepsiCo earned several

industry awards recognizing its position as a top supplier.

The Demand Accelerator’s collaborative approach played a key role in

PepsiCo’s wrapping efforts. One early example of PepsiCo’s collaboration

with retailers involved a convenience store and gas (C&G) retail chain that

wanted to maximize its fountain drink sales, some of which were generated

by PepsiCo’s brands. However, the retailer did not have insight into drink

sales because their scan data only reflected the purchase of a cup, not its

contents.

The Demand Accelerator worked with the C&G retailer to solve this

problem. First, they assembled PepsiCo’s data about the number of gallons

of syrup the retailer used. Together they combined that with the retailer’s

own data about its syrup purchases from other providers. Then, using

advanced analytics, the Demand Accelerator identified specific store and

shopper attributes that influenced syrup usage. Using data wrapping

language, the Demand Accelerator created an insight wrap of analytics-

based soda syrup usage influencers—things like a shopper’s age and

geography—to add value to its core offering of syrup. This insight wrap

was made possible because PepsiCo’s retailers trusted PepsiCo with their

data and also trusted PepsiCo’s analytics. Mutual trust is crucial to effective

collaboration in the joint sphere.

The retailer acted on these insights, changing how it offered fountain

drinks in a subset of its stores. The value the retailer created (for the

retailer) by this action was evident in the increase in fountain drink sales

that followed the change. Once the value of this change was clear, the

retailer introduced the new tactic more broadly. PepsiCo did not directly

charge the retailer for the Demand Accelerator services; instead, it realized

value from the wrap (for PepsiCo) in the increased volume of the retailer’s

syrup purchases.

Over time, the Demand Accelerator played a significant role in

supporting retailer partnerships. PepsiCo’s relationships with its retailers

were initially transactional, but those relationships evolved into

collaborative customer partnerships due largely to the Demand

Accelerator’s activities. In effect, PepsiCo successfully enlarged its joint

sphere with its retailers.

Realizing Value from Wrapping

It is sometimes straightforward to realize value from wrapping, as PepsiCo

did when its retail customers bought more PepsiCo syrup. The realized

value flows directly to the bottom line. As a rule, however, realizing value

from wrapping requires understanding what kind of value and how much

value customers create from your core offering because of the wrap. Based

on this understanding, combined with knowledge of the customer, product

owners can decide how to realize value: raising prices (to charge for the

wrap), selling more of the product to existing customers, selling more

complementary products, selling the product to new customers, or relying

on the wrap to retain customers who might otherwise defect, as you read in

chapter 1.

A wrap can also deliver internal efficiencies. For example, a side effect

of a good wrap might be a reduction in calls to the customer service desk.

Or an equipment wrap that preemptively schedules preventive maintenance

might reduce the need for emergency service during off-hours. To realize

value arising from the more efficient use of people or any other resource,

someone needs to remove or redirect slack, just as they do with

improvement initiatives, so that the savings can flow to the bottom line.

But there’s a complication: these more efficient resources might not

belong to the owner of the offering; they may belong to other functions and

departments. So, getting those efficiencies to the bottom line will require

cooperation from the heads of those units. Fortunately, some efficiencies

(reduced inventories, reduced warranty costs) will flow automatically to the

bottom line.

Figure 4.4

Value realization from wrapping initiatives

To summarize, as shown in figure 4.4, of the value created by the

wrapping initiative, some might be realized for the organization as reduced

costs or increased revenues—money—and some goes home with the

customer. And some value is left on the tree, so to speak. That is, some

value is created but not turned into money by the organization. Instead, that

value might increase the organization’s capacity for innovation, go home

with employees and managers, or take the form of good customer

relationships. The amount of fruit on the tree (the amount of value created)

essentially sets an upper limit on how much value the organization can

realize. In some B2B contexts, how much value the organization can realize

is subject to negotiation with the customer.9

Measuring Realized Value for Wrapping

Organizations should know what value from wrapping is hitting their

bottom line from additional sales and operating efficiencies so they can

make sure they are investing in the right things. Nonfinancial value

(employee satisfaction, customer loyalty, or extra time for innovation)

should be measured as well. Some organizations have accepted methods for

putting a value on their customer’s loyalty or their brand capital. For

example, at one time, Carlson Hospitality estimated that each new loyalty

program enrollment added US$20 of value to its Radisson brand.10 Ideally,

a product owner will have recorded baseline metrics that can be tracked

after the wrap is launched and going forward. This helps the product owner

understand the impact of the wrap.

In the authors’ research, product owners at a financial services company

were great at measuring value from wrapping. For example, the product

owner of one of the company’s affinity credit cards prioritized wraps related

to fraud reduction, which was a top concern for customers of that offering.

Fraud reduction wraps for this card included digital transaction statements

that contained merchant logos, geospatial maps that reduced the time and

effort it took a customer to review their purchases, and email/text alerts

when transactions suggested unusual activity, such as a tip amount that was

disproportionate to the cost of a meal.

Across the financial services company, product owners monitored how

customers reacted to new wraps by conducting experiments that compared

the attitudes and behaviors of recipients of a new wrap to control groups of

customers who did not receive it. The company’s product owners always

tied these customer reactions to changes in product sales. As a result, the

product owner of the affinity credit card mentioned earlier could easily

determine the extent to which a wrap feature increased usage of the credit

card (and thus led to revenues).

The fraud reduction wraps were so intuitive and helpful to the affinity

credit card customers that they led to a reduction in calls to the customer

service center. Although the product owner did not plan for that value, the

call center process owner gladly recognized the efficiencies and redirected

the slack to cover other call center work.

In the public sector, tracing the value of a wrap to the bottom line can be

challenging. For example, a wrapping initiative might get citizens to enroll

in a public health program and save lives, reducing public health costs but

in the long run, not the short run. At a philanthropic organization, a

wrapping initiative might show progress toward accomplishing key goals,

leading patrons to donate more to that important cause. Or a government

agency might need to wrap its offerings just to keep up with citizen

expectations of excellent service and to avoid future taxpayer revolt. In

even these cases, measurement is critical for understanding whether a wrap

is doing its job. And when measurement can be used to report on success to

key stakeholders, public sector organizations can more easily increase or

maintain their inflows from donations, budget allocation, grants, and the

like.

Data Monetization Capabilities from Wrapping

Organizations need to invest in data monetization capabilities to create

useful and engaging wraps (ones that earn high scores on the four As).

Suppose a product owner wants a wrap not just to anticipate and adapt but

also to advise and act. In that case, the organization will need more accurate

data, a faster platform, and deeper customer understanding. Its acceptable

data use capability might need to become more sophisticated, to include

oversight of the use of data by algorithms and the permissibility of acting

on the customer’s behalf.

Like the improving approach to data monetization, the wrapping

approach also requires all five data monetization capabilities described in

chapter 2. And, as with improving, wrapping generates greater returns from

more advanced capabilities.11

Research to consider

Product owners who say their organizations wrap more effectively than

peers reported an average return on investment (ROI) of 61 percent from

their wrapping initiatives, compared to just a 5 percent ROI by product

owners who say they wrap less effectively than peers.12

Organizations that are top performers in wrapping have better capabilities

than bottom performers (whose fans would be blank). Still, they do not

necessarily have advanced data monetization capabilities, as shown in

figure 4.5.13 Organizations can achieve valuable wrapping outcomes

without using machine learning or curated data for their wraps. Still, the

kind of wrap matters: action wraps are difficult, if not impossible, to

execute without advanced capabilities. Organizations do, however, need

baseline capabilities that facilitate customer-savvy wrap development

regardless of whether the wrap offers data, offers insight, or prompts

action:14

• Top wrapping organizations draw on rich customer data assets

regarding customer demographics, sentiments, relationships, core

offering use, and interactions with the organization, integrated to

produce a 360-degree customer view.

• By providing internal access to data and tools through a data

platform built with advanced technology, they allow employees

across the organization to access customer information.

• Their data science capabilities offer statistics skills and

understanding, allowing them to provide insights to customers as

well as to employees seeking to understand how well the

organization is meeting customer needs.

• For top wrapping organizations, sensemaking, by listening to

customers, is critical for surfacing not just core customer needs

but also latent and unmet needs.

• All organizations that wish to pursue wrapping must have some

acceptable data use capability in place to ensure that employees

use customer data in compliant and ethical ways. However, the

research indicates that this capability is difficult for organizations

to build, and even top performers are still working to get the right

new practices in place.

Figure 4.5

The capabilities of top-performing wrapping organizations

Data Monetization Capabilities at PepsiCo

The data monetization practices of PepsiCo’s Demand Accelerator unit

illustrate how much effort PepsiCo invested in capability building and how

it has accumulated the enterprise capabilities required for wrapping

initiatives. Even though PepsiCo had local data monetization capabilities

within each of its major divisions before 2015, they were siloed and often

duplicated. As a result, different divisions were serving the same retailers in

potentially inconsistent ways. The Demand Accelerator was set up to

resolve this problem by centralizing and then diffusing enterprise data

monetization capabilities across PepsiCo.

PepsiCo’s capabilities grew from adopting practices like those used by

top wrapping organizations. Back in 2012, the global IT unit had

established a data taxonomy so that PepsiCo had a “single version of the

truth” for its product sales, worldwide. The IT unit had also adopted master

data management practices and housed product sales data in a single

enterprise data warehouse. These practices built the company’s data

management capability. After the Demand Accelerator was established in

2015, its leaders worked with PepsiCo’s global technology unit to build a

data asset covering 110 million US households. This data asset was

invaluable for identifying pockets of potential growth because it included

not only consumers of PepsiCo products but most of the consumers in a

given geographic region. The data asset about consumers—referred to as

“Most Valuable Shopper” data—was integrated with data assets about

PepsiCo product sales, further strengthening the company’s data

management capability. The Most Valuable Shopper data was carefully

deidentified and structured to ensure that the data’s use would be fully

compliant with legal, regulatory, and ethical constraints. As a result, the

company’s acceptable data use capability grew stronger.

Together with IT, the Demand Accelerator established a cloud-based

platform that allowed it to source and manage more extensive and more

diverse kinds of data, drawn from both inside and outside PepsiCo. This

platform was required to store, manage, and deliver the growing collection

of data assets that fueled the Demand Accelerator initiatives. In addition,

because it was API enabled and cloud based, it allowed secure access by

retail partners, as needed. These practices contributed to the company’s

data platform capability.

The Demand Accelerator hired new expert data science talent and

upskilled their own analysts to enhance the company’s data science

capability. The data scientists created easy-to-use dashboards and reports

for marketing and sales employees across the organization. Engaging with

and learning from these marketing and sales employees helped the Demand

Accelerator better understand the needs of PepsiCo’s retail customers.

These practices helped strengthen the company’s customer understanding

capability.

Over time, people working in the Demand Accelerator packaged some of

the customer-specific wraps they had developed (wraps similar to the earlier

example of the fountain drink optimization insight wrap) into turnkey

applications for other retailers. The applications supported common retailer

use cases, like customizing the product assortment at a specific store based

on the needs of its local shopper base and successfully launching and

managing innovative marketing programs. As PepsiCo’s data monetization

capabilities became more advanced, the number of applications grew into a

suite of wraps named “pepviz” that helped retailers optimize their store

assortments and product sales.

Ownership of Wrapping Initiatives

The ideal owner for a wrapping initiative is the product owner. A person in

this role is accountable to the organization’s leaders for the overall success

of an offering that the organization delivers to its customers or constituents.

This product owner has deep knowledge of the strength and weaknesses of

the offering’s customer value proposition, how well the offering delivers on

the value proposition in the view of customers or constituents, and how well

the value proposition pays off in terms of financial returns to the

organization. For this reason, the product owner should be accountable for a

wrapping initiative regardless of whether the funding comes from the

traditional IT portfolio funding process, the marketing budget, or elsewhere.

The product owner manages a wrap just as she manages any other

product feature or experience. She needs to understand how the wrap is

supposed to impact the product’s customer value proposition. She weighs

the costs, benefits, and risks of deploying the new wrap as part of the

overall product management and development processes. She also

prioritizes the wrap opportunity along with other product-related activities

and investments.

At PepsiCo, the product owner for many Demand Accelerator initiatives

was the customer account manager accountable for the profitability of the

specific retailer or retailers who benefited from a particular wrap. So, for

example, the owner of the fountain drink wrap was accountable for the

C&G retailer account. That customer account manager was best positioned

to understand how to formulate an appealing wrap, allocate budget and

resources to make it happen, and identify what complementary adjustments

would be needed to support this customer account and this wrap initiative.

Without the account owner, the Demand Accelerator might have wasted a

lot of company resources by developing a wrap that was unrealistic,

undesired, or didn’t pay off.

An important note is that the product owner is best positioned to mitigate

the top risk associated with wrapping: value-loss risk. A wrap must meet

service levels that are acceptable to the customer. The product’s value

proposition can deteriorate if the organization deploys a wrapping initiative

that falls below the customer’s expectations. Imagine a wrap that displays

data that is wrong or takes several minutes to load! Product owners will

insist that wrapping delight, not disappoint, their customers.

The product owner plays a vital role for data wrapping initiatives, but as

with improving, wrapping requires getting all kinds of people on board to

ensure success. Yes, wrapping, too, takes a village. Think of all the people

who “touched” the fountain drink wrap at PepsiCo: data scientists and

marketing specialists in the Demand Accelerator; data, systems, and

technology people who worked with the data assets used for the initiative;

sales teams; and people in supply chain and distribution who were needed

to fulfill requests for more products. Indeed wrapping, like improving, is

everybody’s business.

Time to Reflect

Each touchpoint along the customer journey—at purchase, during use, or

while engaging with customer service—offers different wrapping

possibilities. And these possibilities might entail delivering data, insight, or

action to your customers. Regardless of which wrap type makes sense for

your organization and your offerings, you need to have a clear picture in

mind of the customer’s offering-related goals, how the wrap generates

customer value, and how your organization can realize value. Here are the

key points from this chapter to keep in mind:

• Wraps are enhancements to physical or intangible goods or

services you offer to your customers. Which of your offerings

seems most likely to have a value proposition that could be

enhanced with data or analytics?

• Wraps can deliver data, insight, or action to your customers; action

wraps require that you deeply understand your customers’ value-

creation process. Where in your organization would you find that

kind of knowledge of customers? Would you need to partner with

customers to understand how they create value from your

offerings and how that value could be increased?

• Wraps differ in the extent to which they anticipate, adapt, advise,

or act on behalf of customers. Consider any wraps you already

have for your offerings—how well do these wraps anticipate,

adapt, advise, or act?

• Wraps can create value for customers, and an organization can

realize some of that value. How do you realize value from your

existing wraps? Do you have existing metrics or approaches to

measure the value customers realize from your core offerings that

you could exploit to assess customer value creation or realization

from a wrap?

• Wrapping initiatives draw on all five capabilities, not just customer

understanding. Looking back, can you think of a wrapping

initiative that failed because the needed capabilities were

unavailable? What capability-fostering practices did that

initiative require?

• Wrap initiatives should be owned by the owner of the offering that

will be enhanced. What will it take to engage owners of your

offerings in a wrap initiative?

In the process of wrapping some of your goods or services, it would not

be unusual to get to know your customers well enough to see an opportunity

to sell a customer (or a different customer) a completely new solution based

solely on your organization’s data assets. That’s the subject of the next

chapter.

Selling Information Solutions

First, you need to understand what your customer is challenged with. Then consider:

what do you have that can be brought to the table to provide a solution and that can

scale and grow in a changing environment?

—Don Stoller, Healthcare IQ

You’ve made it to the third approach to data monetization: selling

information solutions. Before you picked up this book, there is a good

chance you thought “data monetization” was just another name for selling

data sets. Now you know that data monetization is much more than selling

—it’s improving and wrapping too! In this chapter, you will discover that

selling involves more than selling data—it’s selling insights and action too!

Your organization can package and sell a variety of standalone

informational solutions that help customers solve important problems.

Historically, organizations pursued selling when they recognized that

they had data for which other organizations would pay handsomely. For

example, in the medical supply distribution space, back in the 1990s,

Owens and Minor (OM) accumulated loads of medical supply cost data as it

distributed medical supplies from thousands of manufacturers to hundreds

of hospitals. In 2004, it established a business called OM Solutions that

converted OM’s vast trove of cost data into data assets that that could be

used to create hospital spend analytics solutions. The hospitals used the OM

Solutions offerings to manage their medical supply costs more effectively

and save money.1 In the retail grocery space, Kroger established its 84.51°

business to leverage POS data assets that were delivered to retailers via

marketing analytics tools and advisory services. The retailers used the tools

and services to create more personalized experiences for shoppers across

their purchase journey and to make money.2

After learning about the challenges of the improving and wrapping

approaches, it might seem simpler just to sell your organization’s data to

generate big returns. Warning: it’s a rewarding but higher-risk option. The

selling approach to data monetization involves standalone information

solutions; there is no underlying core offering with a value proposition that

is merely enhanced. Selling organizations must create information solutions

with their own appealing value propositions for which customers would

pay. They must create solutions that meet current market needs and then

adapt and expand the solutions to continue to profit while serving new and

changing market needs. And they must do this while fending off scrappy

competitors eager to enter a potentially high-margin business.

Questions to ask yourself

As you read this chapter, imagine new opportunities for your

organization. Are there problems that customers would pay you to solve?

Can you fill that need by selling an information solution? Do you have

data assets that could be used to inform such a solution?

Research to consider

Survey respondents, on average, reported that selling accounted for 18

percent of their revenues from data monetization activities, making it the

least prevalent of the three approaches in the improve-wrap-sell

framework.3 No doubt, this reflects the complexities of selling

information solutions.

Types of Information Solutions

As data proliferates, more organizations see opportunities to solve others’

problems with their data assets. For example, a medical device

manufacturer—with sensor data assets that reflected patient health—saw

that it could help clinicians offer better diagnosis and care. A custodian

bank—with actual cash flow data assets associated with private equity

funds—saw that it could help investors evaluate and analyze private capital

markets. And in chapter 2, you read that BBVA created a deidentified bank

card data asset that it offered via its BBVA D&A subsidiary. Over the years,

BBVA learned that its asset could help urban planners understand the

economic impact of city decision-making, disaster recovery managers

prioritize relief efforts, and merchants better target and attract customers. In

all of these cases, the organizations expected to leverage their data assets to

sell any of the following: raw data, prepared data, reports, analytics, or

analytics-based consulting services.

Selling might seem to be a good fit for organizations with deep domain

expertise and an engaged customer base.4 However, organizations with an

incumbent business model tend to impose the practices and values of their

existing business on their selling business. This invariably creates high

overhead costs, unnecessary regulatory constraints, bureaucratic processes,

rigid data use terms, or conservative talent management practices, any of

which can hurt the viability and profitability of an information offering.

Organizations need to allow their information businesses to pursue a unique

business model without interference. Notably, OM and Kroger both

established separate business units to build and nurture their respective cost

management and marketing insights businesses. Both companies

recognized that selling was different than their respective distribution and

retail businesses, and the separate units ensured that information solutions

received dedicated managerial attention and resources.

All information solutions fall into one of the same three basic types that

improvements and wraps did: they offer data (data solutions), insight

(insight solutions), or action (action solutions).5 As with wraps, information

solutions create value only in theory until the value-creation process is

completed, usually by the customer. As illustrated in figure 5.1, sellers, in

addition to having little control over the customer and perhaps knowing too

little about the customer, can be far from the action and value creation. That

distance can make it hard for selling organizations to price solutions

correctly or understand how to evolve and shape the solutions over time.

Figure 5.1

How information solutions differ in their scope

Information Solutions That Offer Data

Although all kinds of data sets are increasingly available at no cost via

“open data” websites and public sector initiatives, selling data is still a huge

industry. The global data broker market, which includes companies that

collect and sell users’ internet information, was valued at US$232 billion in

2019.6 In 2022, Verisk, Inc., a firm that serves the insurance and energy

industries, had 19 petabytes of information in its data stores, an insurance

fraud database with more than 1.5 billion claims, and models covering

natural hazards in more than one hundred countries.7 More broadly, the

industry includes organizations that specialize in developing unique

proprietary data assets by combining multiple data sources, ingesting data

from rare sources, generating data based on a platform business or across an

ecosystem, or collecting contributory data from a set of peer organizations

(often competitors!).8

Organizations that focus on data solutions work to create data assets that

customers can easily plug into their own data environments. Data assets that

customers purchase usually fill gaps in their own data assets, allowing the

customer to do analyses or take action that they otherwise could not do. The

cheaper and easier it is for customers to access and use the data assets, the

more appealing the assets will be to the customers, and thus the more

customers will pay for them. For this reason, raw data (data that is

minimally processed) rarely will command as high a price as data that has

been cleansed, standardized, validated, enhanced, and teed up for analysis.

TRIPBAM, a privately held company in Texas, was founded in 2012 to

help travelers reduce their hotel expenses.9 Over time, it evolved to focus

on the corporate travel market, helping organizations’ travel units manage

travel costs and contract compliance. In 2020, TRIPBAM served the travel

needs of roughly half of all Fortune 100 companies. The company offered

all three types of information solutions: data, insight, and action. Let’s look

first at its data offerings.

TRIPBAM developed a portfolio of reports (data solutions) that featured

information of importance to travel buyers, such as negotiated room rates,

amenities adjustments, and corporate program adherence. Large customers

paid TRIPBAM a monthly subscription fee to access the reports and other

services. During the Covid crisis, TRIPBAM leveraged its visibility into

hotel rates to develop novel reports specific to the pandemic. For example,

the company launched a weekly closure report that helped its customers

know whether a hotel was operating or had shut down (another data

solution). TRIPBAM published the closure report for free (such information

was of great interest to the industry and policymakers) to demonstrate the

value that could be extracted from its data assets.

Information Solutions That Offer Insight

As a consumer, you likely are familiar with the consumer credit score, an

insight solution that reveals the likelihood that a person will default on a

debt. Companies that provide credit scoring have developed backend

calculations that are proprietary and quite sophisticated. The scores appeal

to consumers as well as organizations that offer loans or leases, credit cards,

and home mortgages. Employers use them too.

Information solutions that offer insight use analytics to help customers

make better decisions. Scores, benchmarks, alerts, and visualizations help

customers view and understand data in ways that are tailored to their

specific context, helping them prevent or solve problems. However,

customers must use the insights—take some action—to find value in them.

Therefore, organizations maximize the value potential of their insight

offerings by delivering relevant and understandable insights, which fit

naturally into their customers’ workflows.

When TRIPBAM first entered the hotel rate shopping industry in 2012, it

pioneered “clustered rate monitoring” in the hotel industry, which involved

monitoring three metrics for a cluster of hotels within a given area: rate

fluctuations, the best available rate, and the last-room-available rate. These

metrics were the basis for several of its insight solutions. One such solution

was to find better hotel rates and suggest rebooking opportunities.

Rebooking opportunities were insights that could lower travelers’ hotel

costs if they rebooked their hotel rooms. Later, when TRIPBAM began

serving corporate travel buyers, the company sold insights that informed

corporate travel buyers about instances of noncompliance with their hotel

rate agreements. But travel buyers still had to follow up with the hotels and

attempt to extract refunds. In both cases, the value-creation potential of the

insight was quite clear, but customers did not always follow through—take

action—and realize savings.

Information Solutions That Prompt or Trigger Action

Ideally, a seller offers information solutions that trigger action by executing

a task or by doing something on the customer’s behalf. Task automation,

process automation, and process outsourcing are ways for sellers to take

action on a customer’s behalf. These kinds of solutions involve the

organization most deeply in customer value creation. In some cases,

information solutions simply prompt customers to act on insights by

making it very easy or very valuable to do so. Consulting and on-site

support are also action-prompting offerings that leverage the organization’s

accumulated expertise to prompt customer action. The appeal of consulting

and on-site support is that both give an organization a front seat view of

customer value creation.

Over time, TRIPBAM learned to automate action taking in its solutions

when it was possible. For individual travelers, TRIPBAM automatically

rebooked hotel stays when a better deal was identified that matched the

traveler’s preferences and constraints. For travel buyers, when incidences of

contract noncompliance were identified, the company generated automatic

emails to hotels warning them that they would be removed from the buyer’s

travel program unless they complied with specific contractual obligations.

Facilitating customer action ensured that TRIPBAM’s information solutions

created value for its customers, and it also made the company’s solutions

“sticky” because they became embedded in customer habits and processes.

Competitors were less appealing to travel buyers who came to consider

TRIPBAM reports and services as part of their own standard operating

procedures.

For organizations that have advanced data monetization capabilities,

automation is achievable. Automation eliminates customer effort in taking

action, guaranteeing that the customer will obtain value from the solution.

Before customers will accept automated action taking, however, they must

have deep trust in the provider’s intention and ability. Therefore sellers need

to communicate clear rules of engagement that govern automated actions,

and they must establish transparent action taking that they can explain and

monitor.

When TRIPBAM automated the process of securing the best-value hotel

accommodation for travelers for its corporate customers, it was quite a feat.

The service had to cancel an existing reservation and move the traveler to a

new comparable room at a lower rate while complying with corporate

agreements, satisfying traveler preferences, and abiding by hotel

cancellation policies. The service resulted from years spent gaining

experience with and knowledge about the overall rebooking process,

building credibility with its travelers and travel buyers, and investing in

technologies that could support fast, secure, and reliable rebooking

transactions.

Selling Information Solutions at Healthcare IQ

TRIPBAM’s range of data, insight, and action offerings—and its shift to

offering action solutions over time—is typical of selling organizations.

Let’s look at one more seller that experienced a similar journey, this one in

the healthcare field.

Healthcare IQ is a privately held healthcare spend management company

based in Florida.10 It was founded to help hospitals manage their data about

patient billing. Initially, this meant helping hospitals collect, clean, and

standardize their data about patient medical procedures and the associated

medical supplies used for those procedures. This was difficult because

patient billing data was managed in many different systems, often in

inconsistent formats. A hospital could have a single syringe in their systems

that looked like twenty distinct products because it was recorded in twenty

different ways; Healthcare IQ knew all those ways and could map them to

one correct item. Over time, it grew its ability to fix data anomalies like this

(using its proprietary product master catalog). The company also amassed a

trove of hospital expenditure data and a deep understanding of hospital

spend management problems.

Around 2000, the US federal government began to pressure hospitals to

manage their costs. By that time, Healthcare IQ’s leadership was confident

that the company had built up the data monetization capabilities required to

compete in the emerging healthcare spend management industry. In

addition, leaders believed that the data assets the company had meticulously

gathered and curated over the preceding decade (specifically its hospital

expenditure data and its product data catalog) could be used to create

information solutions that would help hospitals drive down costs.

Healthcare IQ’s portfolio of offerings changed over time, beginning with

a focus on data solutions and moving to solutions that prompt action.

• Data solutions: In its first decade of operation, Healthcare IQ

helped hospitals clean and standardize their patient billing data.

Over time, it built a team of clinicians who developed tools and

processes to enrich its product catalog. The company created new

fields that were helpful to hospitals, such as a product

equivalency field that indicated which products could be

interchanged without concern.

• Insight solutions: Healthcare IQ’s offerings evolved to include a

web-based reporting interface that helped hospitals benchmark

their spending against the spending of other hospitals and medical

entities. Over time, Healthcare IQ incorporated interface

visualizations, alerting, and exception reporting to better highlight

for customers precisely what they should be learning from the

reports. In 2011, Healthcare IQ rolled out Colours IQ, a Google

Maps–like experience based on a proprietary tool that delivered

data visualizations via hundreds of thousands of predefined pivot

tables. Colours IQ helped users identify and evaluate potential

savings opportunities using visual features, such as colors, to

indicate spend levels that were above or below expectations.

• Action-prompting solutions: By 2014, Healthcare IQ offered on-

site consulting services to help customers take appropriate actions

based on the insights in its reporting tools. Consultants were

placed with hospital teams to help the teams access, interpret, and

act on savings opportunities. To convince hospital leaders to buy

its consulting service, Healthcare IQ offered a shared-savings

model, earning revenues based on how much it could help the

hospital save.

Creating Value from Selling

Like wrapping, selling creates value at the hands of the customer after the

customer acts on the seller’s data, insight, or action solutions. But this does

not mean that selling organizations passively wait around for that to happen

—quite the opposite. Experienced sellers know how the customer value-

creation process should unfold, including how the information solution will

get used. These sellers also come to expect that a customer will sometimes

drop the ball. Sellers constantly analyze customer behaviors, sentiments,

and needs, proactively monitoring data or insight access, tool usage, or

action taking. When monitoring is proactive, the seller will have time to fix

any failure to act using education, product design, customer service, or

incentives. As with wrapping organizations, selling organizations often

choose to offer solutions further down the data-insight-action value process.

Because selling often involves seizing new market opportunities, new

customers are usually in the mix. It follows that this new customer’s value-

creation process and desired customer experience will take some time for

the seller to appreciate. Here, too, collaborative development can ensure

value creation. As with wraps, information solutions benefit from

development approaches that leverage collaborative customer relationships.

They allow the seller to learn what value the customer is creating and how.

In the case of TRIPBAM, company leaders focused on delivering a

compelling ROI to their customers. To monitor this, they computed a client-

specific ROI by tracking each customer’s savings compared to how much

the client paid TRIPBAM for services. Not surprisingly, TRIPBAM had

virtually no customer churn.

Realizing Value from Selling

Information solutions, like wraps, are typically priced starting with a careful

analysis of how much value is created for the customer. The solution cannot

be priced beyond its potential value to the customer, at least not for long.

For example, Healthcare IQ expected a US$100 million hospital to achieve

at least US$2–3 million in cost savings; TRIPBAM tried to deliver 2–3

percent in overall savings on its customers’ total travel spend (which could

be as much as US$10 million). With knowledge of potential customer value

in hand, organizations can choose a pricing strategy that makes sense for

their specific information solutions and will work for the customers buying

them.

Data solutions are often viewed and priced as commodities. If there are

only a few customers for a data solution, one way to price the data is at

auction. For example, an investment bank that wants to predict market

trends more accurately might be willing to pay top dollar on auction for

exclusive rights to a rare data asset. Those buying data at auction probably

know precisely what it is worth to them. In the case of investment banks, it

could be millions of dollars.

Pricing information solutions that offer insight or that trigger action is

challenging. Still, cocreating with a few customers, monitoring use, or

providing consulting services to them can afford an insider view of the

customer experience. Initially, selling organizations might establish a shared

value agreement whereby they take on the costs and risk of developing an

information solution for a customer in return for some percentage of value

that materializes for the customer. In these cases, sellers often develop

offerings that prompt or trigger action to ensure value creation, the tree of

fruit to be shared. TRIPBAM offered value-sharing deals to its smaller

clients that could not reliably predict the number of monitored reservations

they would need and didn’t want to overpay for TRIPBAM’s monthly

subscription services. The gainsharing model entitled TRIPBAM to receive

25 percent of the realized savings for any reservation that it rebooked on the

smaller client’s behalf.

Figure 5.2

Value realization from selling initiatives

As with wrapping, information solutions create value, some of which is

realized for the organization in the form of money and some of which goes

home with the customer whose problems are solved, as shown in figure 5.2.

And, as always, some value is left on the tree, so to speak; some value is

created but not turned into money by the organization. In the case of

information solutions, that value might take the form of brand capital, good

customer relationships, or innovative capacity. And, as noted earlier, the

amount of value that can be realized cannot exceed the amount of value

created. You can only pick as much fruit as is on the tree.

Another consideration when pricing information solutions is the position

of the information solution in a competitive marketplace. What a customer

is willing to pay depends on both how much potential value the solution

promises (assuming the value-creation process will happen) and the price of

alternative solutions. (If the solution is competitively distinct, alternatives

don’t matter, but how much value the customer expects to create does.) An

information solution—like any type of offering—is competitively distinct

when it is rare, cannot be imitated, and can withstand the threat of

substitutes.11 For example, when Healthcare IQ’s offerings were first on the

market, they were one of a kind, so they were definitely rare.

A competitively distinct solution is difficult to imitate. Often, its

mechanics are complex, hidden, or protected from imitation by patents.

Healthcare IQ worked with a technology partner to develop a unique

visualization offering that their hospital customers loved. It was easy to

understand and use, but it was not easy to reverse engineer or recreate from

scratch. Healthcare IQ’s CEO believed so strongly in the underlying

technology, called fractal maps, that he purchased the fractal map company

so that Healthcare IQ would hold the fifty-plus patents associated with the

technology. The CEO wanted to prevent competitors from partnering with

that same tech company to create similar solutions.

A competitively distinct solution can withstand the threat of substitutes.

It’s hard for customers to find a comparable replacement. An organization

can do this by developing features and benefits that customers can’t find

elsewhere. This is a key reason why sellers also do a lot of wrapping! They

rely on wrapping to boost a solution’s customer value proposition again and

again.

In fact, the biggest threat to information solutions is the copycat provider

—the cheaper substitute. Because of this risk, an organization not only

needs to offer a unique and desirable information solution but also must

maintain the solution’s uniqueness and desirability over time. Otherwise,

revenue streams will dry up. Years ago, when Magid Abraham, then CEO

of Comscore, was speaking to a class about selling information solutions,

he said, quite passionately, “Information products are obsolete upon

launch!”12 The information solution marketplace can be ruthlessly

competitive. Competitive pressures force information businesses to

continuously innovate and improve their information solutions so they can

keep their solutions distinct from those of competitors.

So, how do information businesses create solutions that maintain their

competitive advantage over time? They draw on the following sources of

value that allow them to create offerings that are rare, difficult to replicate,

and difficult to substitute:13

• Unique data that is sourced, combined, or enhanced to produce

one-of-a-kind data assets.

• Cost-effective, proprietary platforms that can process data and do

things that competitors simply can’t do or can’t do as cheaply.

Proprietary platforms are notoriously difficult to reverse engineer.

• Sophisticated data science and data scientists who are passionate

about solving problems with data. While one algorithm might be

replicable or substitutable, a complex combination of algorithms

that is the brainchild of sophisticated data scientists is far less

likely to be.

• Domain expertise that sellers promote by having their domain

experts speak at conferences, sit on standards boards, and publish

industry white papers and academic articles.

• Customer empathy that helps sellers understand and appreciate

customer problems deeply. It also helps them identify ways to

monitor and measure their ability to create customer value.

Note that these sources of value are rooted in data monetization

capabilities. This is yet another reason organizations engaged in selling

initiatives need advanced capabilities.

Capability Considerations for Selling

While Healthcare IQ developed a successful business model around selling,

selling is still inherently higher risk than improving or wrapping. Selling

organizations face the need to develop and grow new markets, the need to

establish a new business model, the need to keep up with data privacy laws,

and the need to constantly fend off competitive threats. To overcome these

challenges, organizations that sell information solutions rely heavily on

advanced enterprise data monetization capabilities in all five areas, as can

be seen in figure 5.3.14

Organizations that are top performers in selling information solutions

report having the following data monetization capabilities:

• Unique, high-quality data assets that are easily combinable,

including with their customers’ data.

• Advanced technology data platforms that offer secure, fast, and

reliable access to both internal and external users.

• The ability to use statistics to extract sophisticated insights from

vast quantities of data.

• The ability to experiment with solutions to uncover customer

needs and wants so that new market needs are constantly served

with appealing offerings with a high likelihood of creating

customer value.

• Automated data use controls that ensure the protection and

oversight of sensitive and valuable information at scale.

Figure 5.3

The capabilities of top-performing selling organizations

The final point warrants further attention. Modern life has become highly

quantifiable and connected. As a result, data assets that are used to develop

information solutions can contain sensitive data. They can reflect the

behaviors of individuals as customers, citizens, employees, students, and

activists. As of this writing, too many organizations lack adequate local,

federal, and global safeguards associated with data asset sourcing,

manipulation, use, and protection. Having an advanced acceptable data use

capability is critical to navigating ethical challenges.15 An organization

must have an enterprise ability to make sure that data asset use is not only

compliant with regulations but also consistent with the values of its

stakeholders. In fact, when considering ethics, companies might want to be

more restrictive with their data than current regulations stipulate.

Regrettably, a deeper treatment of this complex topic is outside the scope of

this book.

As noted earlier, more advanced capabilities are associated with better

data monetization outcomes. In the case of selling initiatives, however,

advanced capabilities are a requirement rather than an option.

Data Monetization Capabilities at Healthcare IQ

When people look under the hood of an information business, they often are

surprised by the sophistication and innovation of the data monetization

practices they see. Such practices are often a necessity. A selling

organization might have a hard time finding commercial technology

powerful enough to process its massive amount of data, so it builds its own

hardware and software. A selling organization might need to establish

credibility before entering a new market with a solution, so it hires the most

highly regarded data scientist in the field. A selling organization might need

to reassure its investors of the security of its sensitive data assets, so it

establishes data oversight methodologies that go well beyond regulatory

requirements. Regardless of whether a seller needed to adopt innovative and

sophisticated practices or simply thought it wise to do so, it’s fair to say that

advanced data monetization capabilities are part of the game at well-

established selling organizations.

This was certainly the case at Healthcare IQ. Over time, by need and by

choice, the company adopted increasingly advanced ways to manage,

distribute, and oversee its data assets and effectively serve customers.

Leaders relied on technologists, systems integrators, content specialists,

sales account managers, and customer service providers to propose helpful

practices—or identify needs for practices—and incorporate them into

operations.

Healthcare IQ established a data management capability as it developed

a way to standardize, match, and validate data ingested from its hospital

customers’ transaction systems. At first, data problems were fixed

manually. Then as the team gradually identified the root causes of

problems, it established business rules and automated the fixing using

custom workflow software, resulting in increasingly cleaner data assets

over time. The company also developed tools and processes to enrich the

data. Enrichment activities ranged from mapping products to the correct

manufacturer, reviewing and tagging products for equivalency, and

classifying products so that analysts at Healthcare IQ’s hospital customers

could develop better reports.

At the heart of Healthcare IQ’s data platform capability was a proprietary

custom-built data warehouse that was managed by technologists with skills

in data architecture, virtualization, database development, infrastructure,

open source, and software engineering. The technologists learned how to

serve the company’s internal data processing and distribution needs as well

as the needs of hospital customers. For the latter, they built out faster and

more efficient ways for the hospitals’ IT people to submit hospital data; for

example, they developed a simple interface that hospitals could use to

check whether their data files met Healthcare IQ loading specifications.

This avoided subsequent problems that might have occurred from bad,

mislabeled, or missing data fields.

Healthcare IQ was ingesting data from multiple hospital customers, so it

began to develop data assets based on this aggregated data (after securing

permission to do so). Data analysts used the assets to calculate benchmarks

and indices and to create reports to solve a hospital’s cost management

problems. As mentioned earlier, in 2011, Healthcare IQ introduced Colours

IQ, an advanced analytics tool that delivered data visualizations via

hundreds of thousands of predefined pivot tables. The CEO viewed the tool

as an essential contribution to the company’s data science capability.

Several years later, Healthcare IQ hired an AI expert to explore ways for the

company to benefit from machine learning.

Healthcare IQ sales and service teams were instrumental in building the

company’s customer understanding capability. The team members

interacted with customers during weekly phone calls, informal

conversations, and emails; cocreated novel offerings with customers during

consulting engagements; and learned about customer needs during quarterly

business reviews, on-site training, and routine support. For example, during

support, a customer might ask for a particular attribute to be added to a

report. Team members mined support experiences to identify ways to add

functionality to existing products, develop new offerings, or automate

customer processes, submitting ideas to a system that tracked them.

Management discussed and prioritized submitted ideas at a weekly staff

meeting, and ultimately, high-priority ideas flowed into product

development. To get deeper insight into customers, Healthcare IQ hired

people who previously worked in customer or partner organizations when it

could.

Finally, Healthcare IQ developed its acceptable data use capability to

ensure that hospitals were comfortable with the company’s guardianship of

their data. Initially, Healthcare IQ established HIPAA-compliant processes,

policies, and procedures. Later, leaders pursued HITRUST certification,

which would corroborate Healthcare IQ’s conformance with security best

practices. The company sought external validation of its efforts, and it

promoted such confirmation to customers, partners, and other stakeholders.

Healthcare IQ’s advanced data monetization capabilities positioned the

company to cope with the turbulent dynamics of the healthcare spend

market. Hospitals that had historically focused on understanding their costs

were prompted by new US government regulation to understand their costs

within the context of clinical outcomes. New competitors—including

software providers, consultants, distributors, industry associations, and

start-ups—began to offer spend analysis solutions to their hospital

customers. Customer expectations grew as hospitals developed savvier

talent and modernized their systems. Despite such strong forces, Healthcare

IQ drew on its capabilities to adapt accordingly and stay competitive.

Ownership of Selling Initiatives

Like wrapping initiatives, selling initiatives should also be led by a product

owner. However, in the case of selling, the “product” is an information

solution. (Remember, this book uses the term information solution owner to

distinguish between wrapping and selling owners.) Information solution

owners manage an informational product with its own value proposition,

whereas product owners use wraps to enhance their core product’s value

proposition.

An information solution owner is accountable for the overall profitability

of the revenue streams associated with selling. Because of the specialized

expertise needed for this type of role, organizations often recruit seasoned

professionals from information businesses, technology companies, or

successful digital-native companies to be information solution owners.

These people bring a solid customer-centric mindset and experience in

planning, developing, and delivering information offerings to the role. The

information solution owner manages the costs, risks, and benefits associated

with the information solution.

You can think of an information solution owner as the mini-CEO of an

information solution, coordinating the disparate activities required to

produce, market, and sustain it, including solution design, compliance,

sales, marketing, and IT services. Like a CEO, the owner of an information

solution depends on expertise and commitment from across the enterprise.

In an information business or a business unit devoted to information

solutions, virtually every employee is involved in some aspect of the

information solutions: design, compliance, sales, marketing, after-sales

service, and, last but not least, IT services. So it should not surprise you that

selling organizations employ people at all levels who deeply understand the

customer problem domain—whether it be hospital health costs or hotel

travel spend—and passionately want to help customers solve problems

using data. As a result, selling, too, is everybody’s business!

Time to Reflect

In theory, every organization that has data can use it to create data assets

that can give rise to information solutions. If your organization wants to

pursue the selling approach, start with important problems that someone,

somewhere, will pay your organization to solve. Here are the key points

from this chapter to keep in mind:

• Instead of thinking about ways to sell your vast troves of data,

think about what customer problems could be solved using your

data assets. Who in your organization knows the most about the

important problems customers are struggling to solve?

• Once you’ve identified a customer problem that your organization

could solve as long as the customer took some specific action,

you will need to work to ensure that the customer actually takes

that action and realizes value from it. Which of your customers

might work closely with someone in your organization on this?

• It’s essential that your organization’s information solutions be

competitively distinctive. What distinctive assets does your

organization have that would make your information solution

rare, hard to imitate, or difficult to do without?

• Organizations that are adept at selling have high levels of data

monetization capabilities. Where is your organization in terms of

accumulating these capabilities? Which capabilities need to be

addressed first? What would be the best way to go about building

these capabilities?

• To offer an information solution, you must establish a supporting

business model. Where in your organization would you find the

new market development, product strategy, and other kinds of

expertise your information business would need?

You should now have a good handle on the range of data monetization

initiatives that can make money for your organization— improving,

wrapping, and selling—and what it takes to pull them off. In the next

chapter, you will learn about the ideal organization context for data

monetization: a data democracy.

Creating a Data Democracy

The more we increase access to data, the more we enable curiosity and innovation.

—Rob Samuel, CVS Health

At BBVA, Microsoft, PepsiCo, Healthcare IQ, and all the organizations you

have read about so far, people of all kinds are inspired to engage in data

monetization. They are rewarded for questioning the status quo, sharing

ideas, adopting novel practices, changing habits, and contributing to

organizational goals. They believe that data is valuable, is essential, and

plays a role in the organization’s success. This kind of organization, so

conducive to making money from data, is called a data democracy.

It takes a lot of effort to get the average employee ready and willing to

participate in the data monetization movement. Part of the challenge is

rooted in the old problem of data versus domain knowledge. Domain

experts (accountants, marketers, nurses, civil servants, factory workers,

sales associates—anyone with expertise in a part of an organization) and

data experts (analysts, data scientists, dashboard designers, database

administrators) each have something important to offer to an improving,

wrapping, or selling initiative. For example, to fix a process glitch, you

need a process manager to interpret the problem and a software developer

to write the code. But before coding can start, the developer must

understand the problem and the process manager must recognize the

potential of data assets and data monetization capabilities. It’s tricky to

come to a common problem understanding, using the same language, and to

agree on the optimal use of these data monetization resources. Turf battles,

skill gaps, and politics get in the way. Nevertheless, leaders of data

democracies actively manage through these hurdles and design their

organizations for success.

Data monetization resources are the full set of resources that speed up

data monetization initiatives, including data assets and data

monetization capabilities. Data monetization capabilities may be found

in people with expertise or expertise embedded in tools, routines,

policies, forms, software, and so on.

In short, your organization won’t become a data democracy organically.

Data and domain experts must be motivated to learn from each other.

Without a deep understanding of the organization’s needs, data experts will

be hard pressed to develop the most useful data monetization capabilities

and the most reusable data assets. Shared knowledge—more data savvy

among domain experts and more domain savvy among data experts—is the

key to valuable innovation as well as the diffusion of those innovations—

scaling and reusing them. Innovation and the diffusion of innovation are

achievable in data democracies. This chapter describes the specific

organizational design elements that underpin a successful and sustainable

data democracy: data-domain connections and data democracy incentives.1

A data democracy is an organization with pervasive employee

appreciation of, access to, and use of an organization’s reusable data

assets and data monetization capabilities (i.e., its data monetization

resources).2

A question to ask yourself

What keeps your domain and data experts from collaborating to leverage

your organization’s data monetization resources?

Data-Domain Connections

Imagine that all the “data” people in an organization were colored red and

all the “domain” people were colored blue. As these red and blue people

regularly interact, share what they know, and learn from each other, their

knowledge blends and they become less red or blue and more purple. They

develop a shared grasp of data in their particular organizational context. A

data democracy is populated by purple people!3

Organizational design is commonly thought of as the way in which

workflows, authority relationships, and social ties are organized within the

organization. In the case of data democracy, workflows, authority

relationships, and social ties are configured into structures that blend red

and blue people. The blending occurs by virtue of data-domain

connections: organizational structures linking data experts and domain

experts that facilitate knowledge exchange and learning.

Data-domain connections are structures that facilitate knowledge

exchange between data experts and domain experts.

Dr. Ida A. Someh, a long-time collaborator of the book’s author team,

studied how relationships between analytics groups and business-domain

groups can be configured to facilitate knowledge integration in data-driven

organizational initiatives. She found five common data-domain

connections: embedded experts, multidisciplinary teams, shared services,

social networks, and advisory services. (See figure 6.1.) These five

connections are different means of knowledge sharing—creating purple

people—crucial to both innovation and the diffusion of innovations across

the organization. They work differently, and they work together. Think of

these connections as tools in your organizational design toolkit, the data

democracy special edition toolkit. Organizations can use any and all of the

five connecting structures; ideally, organizations should support enough

structures to yield as much of a data democracy as they need.

Figure 6.1

Five data-domain connections that facilitate knowledge exchange and learning

The connections facilitate two-way collaboration, conversations, and

learning. They build on and help consolidate any knowledge gained in

formal training experiences. For example, if a domain expert takes a

statistics course, a data expert can help to apply that new skill to a particular

problem. If a data expert takes a course in marketing, a domain expert from

marketing can help contextualize concepts from the course to the specific

organization. The connections make it easier for domain experts to become

aware of, access, and use data assets and data monetization capabilities as

they engage in improving, wrapping, and selling initiatives. The

connections also make it easier for data experts to understand how to make

data monetization resources more valuable to the organization. The more

knowledge transfer is activated, the more organizations can fully develop

and exploit superior data monetization resources. When data democracy is

advancing too slowly and data monetization assets and capabilities are

stuck in silos, organizational leaders might find it beneficial to introduce a

few additional connecting structures. Let’s step through each one in turn.

Innovation Connections

Two of the connections link data and domain people in ways that foster

innovation: embedded experts and multidisciplinary teams. (See the top of

figure 6.1.) With innovation connections, data and domain people exchange

knowledge and generate new and improved tasks and processes and new

and enhanced products and solutions.

For example, when an organization embeds a data expert full time in a

marketing department, it is easier for marketing employees to find new

ways to exploit existing data assets in their daily work. Thus, the

organizational capacity to envision and undertake big new initiatives gets

stronger. Maybe the data expert knows how to use an algorithm to identify a

“next-best offer” (the most relevant thing to offer a particular customer at

that moment). The data expert would help the marketers experiment with

using this algorithm to target offerings to different customer segments. The

result will be new knowledge on both sides of the connection: the data

person will know more about this marketing situation, and the marketing

people will know more about next-best offer algorithms. They will both be

a little more purple. With a better understanding on both sides, the

marketing department (including the embedded data expert) might begin

testing the effectiveness of AI-suggested next-best offers. The test results

might inspire a new improving initiative to make the process of selecting

next-best offers faster and more precise, potentially reducing some costs

and increasing sales. This is how embedded experts foster innovation.

Similarly, when an organization assembles a multidisciplinary team to

carry out some initiative, perhaps one to solve a customer churn problem,

they are ensuring that both data and domain perspectives (a variety of

domain perspectives, probably) will inform the solution. Picture this:

marketers in an organization are stuck in a rut and are using outdated tools

for managing customer churn. A multidisciplinary team is formed, charged

with proposing a machine learning approach for customer churn

management. The data scientists would share contemporary ways to predict

customer churn based on both internal and external data. Sales domain

people would explain how salespeople currently connect with customers to

keep them satisfied. Marketers would contribute timeless customer

retention principles. As the team members share and absorb new

knowledge, they formulate ideas about managing customer churn and

propose an improving initiative. Notably, not only does the

multidisciplinary team develop a meaningful data monetization initiative

that solves the customer churn problem, but the people on the team have

each become more purple. These purple people are more capable of

accessing, contextualizing, and using data related to customer churn and

machine learning to create new innovations.

Diffusion of Innovation Connections

If an organization only draws on embedded data experts and

multidisciplinary teams, then over time, localized silos of innovation will

proliferate. This is why connections that promote the diffusion of

innovations—shared services and social networks—are essential. (See the

bottom of figure 6.1.) These connections help spread innovations of all

shapes and sizes to other parts of the organization. Diffusion occurs when

people reuse, rather than reinvent, innovations that exploit data assets and

capabilities. The reuse of process improvements across similar

organizational contexts is one way to increase the value created and realized

from an improving initiative. Wraps can also be reused across related

product lines if additional product managers become aware of them.

Sometimes diffusion happens spontaneously because an innovation (e.g., a

new platform that eliminates paper-based processing) is such an obvious

improvement that it’s an easy move to replace the status quo. But usually,

even a brilliant new or improved tool, process, or product needs a nudge to

spread because spreading is rarely completely costless (e.g., people need

training to use the new platform).

When an organization sets up a shared service unit to deliver standard

reporting software and templates, it is making it easier for domain people to

apply those tools, adopting them as is or customizing them. Shared services

are great for diffusing innovations far and wide; they offer a one-to-many

relationship. For example, say the organization’s product-line unit creates a

dazzling sales dashboard that people in other units drool over. A shared

services group (the one) can spread the dazzle to any other unit that is eager

for their own drool-worthy dashboards (the many). In addition, the shared

services group can offer features like common dashboard metrics with

suggested data sources; ideal colors, visuals, and other user interface

tactics; and self-service options for dashboarding training.

Social networks, on the other hand, use many-to-many relationships to

diffuse innovations. These connections bring together red and blue

employees with common interests but different knowledge. Using social

networks, data and domain people can ask each other questions and provide

answers. Social networks can be virtual, like a Slack community, or

physical, like a data science conference or event.

The Advisory Services Connection

There is one data-domain connection—advisory services—that facilitates

both innovation and diffusion. (See the middle of figure 6.1.) It’s a super

connection and works like a consulting model: consultants learn from their

current engagement so they can spread lessons and practices to future

engagements. Everybody learns, especially the consultant. More people turn

purple, and the data democracy grows.

A lot of organizations have advisory services. They are often centers of

excellence that work with people all over the organization to solve specific

problems. Sometimes advisory services are part of the enterprise hub of a

data transformation office or in the chief data office. Centers of excellence

can also be smaller and more local, serving a specific organization area, like

research and development or a vertical line of business. Wherever they sit,

the advisory services people transfer knowledge to and from their own unit,

learning about organizational needs and spreading new knowledge about

data monetization resources throughout the organization. They spread the

wealth, so to speak.

Note that some organizations create this one structure (often as an

enterprise center of excellence) and stop there. This results in innovation

and diffusion of innovations for sure—but not enough of it. In large

organizations, a single advisory services connection will quickly become a

bottleneck and slow down the growth of a data democracy.

Connecting Structures at Microsoft

Chapter 3 described several improving initiatives that were underway at

Microsoft when the company first shifted its business model from being

product based to delivering cloud services.4 Remember how finance

shortened the time it took to get financial analyses into the hands of sales

personnel? And how enterprise sales reduced the administrative workload

of salespeople to give them more time for customer-facing activities?

The total number of improving initiatives and the amount of innovation

introduced at Microsoft during this time was staggering. One of the reasons

for the abundance of activity was the company’s thoughtful use of

organization design.5 Microsoft used all five connecting structures to

facilitate improving initiatives and elevate people’s ability to exploit their

enterprise data assets and data monetization capabilities, which fueled the

company’s business model transformation. In addition, these same

connecting structures ensured that the data monetization resources that were

made available to initiative teams were what those teams needed.

Microsoft used embedded experts and multidisciplinary teams to help

develop new work processes and new data capabilities. For example, the

data experts embedded within the enterprise sales unit were instrumental in

establishing the Microsoft Sales Experience platform and helping develop

the new enterprise sales processes that the salesforce wanted. Other units

like HR and marketing had similar embedded teams of experts and enjoyed

similar innovative outcomes.

At times, people from different parts of Microsoft came together in

multidisciplinary teams to achieve a target goal. For example, the data

science group and facilities organization—with support from people in legal

and HR—collaborated to optimize the company’s energy consumption. This

team needed to be multidisciplinary because the problem it was solving—

designing “smart” building heating and cooling solutions—was a

multidisciplinary problem.

Nadella himself leveraged a kind of multidisciplinary team to develop a

dashboard with metrics that served as leading indicators for the key pillars

of the Microsoft business. To help identify new data sources to generate the

metrics, he hosted an internal dashboard-building hackathon. Business units

across Microsoft collaborated on building a senior management dashboard;

the effort identified both the systems that held critical data and the business

owners accountable for results. The effort resulted in a new approach to

measuring the success of Microsoft’s transition to a cloud services business

model.

As innovations sprang up in one place, Microsoft leveraged shared

services to diffuse innovations that would benefit others. For example,

leadership invested in data-specific shared services groups, including one

for business intelligence that delivered templates, standard ways of

reporting, and dashboarding across the company. Other shared services

groups were responsible for diffusing other kinds of innovations: standard

sales territory geographies (owned by the data management services group),

building occupancy-related AI models (owned by the data science services

group), and emergent policies to comply with new General Data Protection

Regulation (GDPR) requirements (owned by the data governance services

group).

The business intelligence services group created social network

communities using Microsoft’s own social network platform so that users

with shared interests and concerns could engage with one another to surface

challenges and share ideas. These communities exchanged novel ways of

reporting data and deriving insights; these innovations had previously only

been used in the local business units where they originated.

Finally, Microsoft benefited from the innovation and diffusion effects of

advisory structures. For example, the CIO established a dashboard team that

consulted with Microsoft business executives to help them with their

dashboards. The dashboard team sat down with each executive and created

dashboards customized to their needs and preferences, resulting in

widespread dashboard rollout and use. As a result, these advisors got

increasingly in tune with senior management’s dashboarding needs,

continually learning new ways to meet those needs and delivering better

and more helpful support.

Data Democracy Incentives

To achieve their data democracy goals, leaders must do more than put a

smart organizational design in place. They also need to ensure that people

interact with their data or domain counterparts and learn from each other—

especially about the availability of reusable data assets and data

monetization capabilities. Employees may have little time or inclination to

interact with others, never mind engage in improving, wrapping, and selling

initiatives. Leaders must activate a smart organization design by providing

incentives to people so they will engage in connecting, innovating, and

diffusing those innovations. Otherwise, any emerging new processes or

offerings (if there are any) will not become the new normal across the

board.

To encourage people to seek out new knowledge and learn from their

available connections, organizations should consider using incentives to

induce employees to move the organization toward a data democracy. As

illustrated in figure 6.2, power, social norms, and value propositions are

three types of incentives that organizations can use to motivate employees

to become more purple so they can make full use of data monetization

resources.

Power

Leaders can use the power inherent in their formal or informal authority to

get their employees to adopt and use analytics tools, attend training, or

contribute their experiences to forums. (See the top of figure 6.2.) Leaders

use formal power when they require behavioral change and informal power

when they make it clear that they expect all employees to change their

behavior. They communicate these expectations by linking performance

evaluations to data use and by recognizing and rewarding employees’

success with data.

Nadella, for example, clearly signaled his expectations by being an

enthusiastic early adopter of the dashboard created in Microsoft’s

dashboard-building hackathon. He began actively using the dashboard to

inform his decisions, and leaders across the company soon followed suit.

Once Microsoft’s business intelligence platform was widely available,

business unit leaders became accountable for their employees’ dashboard

use. If adoption wasn’t 100 percent, leaders contacted the employees’

managers, soliciting a plan to remediate. Microsoft’s management also

established new performance metrics to push the use of connecting

structures: they adjusted employee incentives to include “collaboration

across workgroups” as one of the three core pillars on which individuals

were assessed and rewarded.

Figure 6.2

Three kinds of data democracy incentives

Social Norms

Employees are more likely to use a new dashboard, call on a customer

based on an analytics-based alert, or search the enterprise data catalog for a

new data source when their peers are doing the same thing, especially if

their peers can assuage their misgivings. (See the middle of figure 6.2.)

There is a virtuous circle effect to social norm motivation: employees

provide help and support to their peers and, in so doing, raise expectations

that others will follow suit. For example, Microsoft’s internal social

network, Yammer, served as a source of peer support for using the

company’s dashboarding tool. Yammer exchanges about the tool

encouraged employees to embrace the tool while also providing support for

those who were having trouble adapting it for their specific needs.

Microsoft leveraged social norms, in part, by making the adoption of

data-fueled decisions visible. Nadella’s hackathon-built dashboard included

a scorecard based on data supplied by many, but not all, business units

across the company. After the scorecard was launched, business units that

hadn’t initially contributed data rushed to do so; they, too, wanted a

presence on the CEO’s dashboard.

Value Proposition

Leaders tasked with driving new data monetization initiatives often find

themselves trying to win over colleagues to get them to contribute people or

funding to it. A clear value proposition—what data monetization outcomes

will mean to those involved in the initiative—can encourage people to

participate in a new improving, wrapping, or selling initiative. (See the

bottom of figure 6.2.) The value proposition becomes clear when leaders

share success stories that showcase valuable outcomes for many

stakeholders. A clear value proposition is particularly apropos for initiatives

where multidisciplinary teams are needed—each of those distinct

disciplines (or domains) might be seeking different outcomes!

Microsoft took active steps to articulate the value proposition inherent to

its transformation. Nadella frequently spoke about the role and value of data

in the company’s transformation, both to external stakeholders and within

the company. Across the company, business leaders articulated the value

proposition that was specific to their people. In the case of enterprise sales,

for example, sales leaders spoke about how the Microsoft Sales Experience

platform made work simpler and more fruitful for employees who were

engaged in fieldwork. As employees used the system and became better at

recording data about their customer interactions, they noticed that

predictions and alerts regarding their customers grew more accurate and

helpful. And, as a result, they closed more sales. The value propositions

associated with the transformation were clearly communicated, and they

triggered changes in behavior.

People need to be persuaded and encouraged to support their

organization’s desire to innovate with data. Incentives like power, social

norms, and value propositions can increase the likelihood that people will

connect with their data or domain colleagues and share expertise and data

monetization resources.

Time to Reflect

Organizations become data democracies by removing barriers for domain

experts to become aware of, access, and use data assets and capabilities and

for data experts to learn how to develop the right ones. This transformation

results in a broad upsurge in the organization’s ability to exploit data assets.

Leaders who link data and domain people enable knowledge sharing,

inspire learning, and generate innovations of all kinds. Connections that

link local efforts with centralized efforts help surface and sync up localized

innovations so they can be publicized and scaled across the organization. To

activate connections, leaders can institute “carrot and stick” techniques such

as establishing award programs that recognize employees for using data (a

carrot) and establishing accountability for data use when evaluating

employee performance (a stick). Here are the key points from this chapter

to keep in mind:

• In a data democracy, everybody in the organization can participate

in data monetization initiatives. Where in your organization are

people willing and able to be part of an improving, wrapping, or

selling initiative?

• Organizations use five data-domain connections to unleash

innovation and the diffusion of knowledge. Which of the

connections are most commonly found in your organization? How

can you develop other forms of connection?

• In a data democracy, everybody in the organization knows how to

access and use the organization’s data assets and data

monetization capabilities, if they need them. What organizational

structures are most helpful in connecting people with capabilities

in your organization?

• Even in a democracy, people need the motivation to embrace

learning from others and to change their habits. Which incentives

is your organization using to encourage more fruitful use of data

assets? What additional incentives should your organization

consider adopting?

• Innovations that are diffused as widely as possible deliver much

greater payback. Reflect on a recent experience working on any

data initiative where the team developed new practices, such as a

process to automate data quality. Did that practice spread to

other initiative teams or not? If yes, what connections or

incentives were in place? If not, what barriers were in place?

This chapter focused on describing the two key elements of a data

democracy. There is one more thing to know about data democracies: they

need direction. The next chapter is about developing a guiding vision for

your data monetization strategy.

Data Monetization Strategy

Having a data monetization strategy is devilishly helpful. It forces you to be clear in

your thinking. It also helps you decide what to do.

—David Lamond, Scentre Group

You’ve read about the five data monetization capabilities (data

management, data platform, data science, customer understanding, and

acceptable data use), the three kinds of data monetization initiatives

(improving, wrapping, and selling), and the five data democracy

connections that facilitate organizational innovation and diffusion. Put into

action, these frameworks will propel your organization forward, but first

you must ask, Where do we want to go?

The frameworks serve as avenues that can lead an organization toward

different ends. The same framework components can solve different

problems or achieve different objectives. You’ll need a north star to chart a

clear course. Remember the CarMax example from chapter 1? Every

CarMax employee contributes to the collective mission: either they are

trying to sell more cars or they are trying to buy more cars. What’s

important to your organization? Without a north star, it’s hard to discern the

answer to questions like the following: Which should we pursue, improving

or selling? Which capability needs the most attention, data science or

customer understanding? Which parts of our organization need to be better

connected to data assets and capabilities?

You can think of the frameworks as ingredients laid out before a skilled

chef (that’s you!). The chef can combine the ingredients into several

different delectable dishes. But to get started, she needs a vision for the

meal and an understanding of what type of palate it should satisfy. Then she

will know where to begin.

Frameworks (ingredients) in hand, you now need a data monetization

strategy. A strategy includes a goal and a plan for reaching that goal. This

chapter focuses on finding a north star—a vision—for what the organization

hopes to accomplish by monetizing data. A strategy for data monetization

sheds light on the best application of the data monetization frameworks and

what outcomes to expect from applying them. The clearer your north star,

the more focused you can be as you build capabilities, invest in initiatives,

and design your data democracy.

A data monetization strategy is a high-level plan that communicates

how an organization will improve its bottom line using its data assets.

It is a component of an organization’s data strategy.

Questions to ask yourself

Does your organization have a data monetization strategy today? If so,

who was responsible for developing it and sharing its contents?

Setting Direction with a Data Monetization

Strategy

Strategies are high-level plans that communicate what goals an organization

wants to achieve and how it will achieve those goals. A strategy focuses

resources, energy, and attention on some objectives rather than others.1 No

organization can “do it all” because no organization has limitless resources

and managerial attention. All organizations, instead, are constrained by

fixed amounts of money, people, time, energy, enthusiasm, patience, you

name it. As a result, organizations rely on clear strategies to help people

decide when to say yes or no, where to spend their time, and what results to

track.

A business strategy outlines an organization’s plan for achieving specific

business goals. A digital strategy is a plan that focuses on goals related to

digital technology and digital ways of working. It explains what an

organization will invest in to achieve those goals. A data strategy lays out

an organization’s goals and plans for managing and exploiting its data.2 It’s

a tall order to juggle and integrate so many diverse plans. It therefore helps

to think of all these strategies as nesting into each other (see figure 7.1). In

fact, the elements of a data monetization strategy—data monetization

initiatives, data monetization capabilities, and efforts to establish a data

democracy—serve as critical pieces of a general data strategy. (There are

plenty of other elements that would be found in an organization’s data

strategy, which would address issues like data security, vendor sourcing,

and talent management.)

Figure 7.1

A data monetization strategy as a component of an overall business strategy

Different readers will find themselves in different strategic situations.

Some will work for organizations with clear business strategies that their

leaders regularly articulate and reinforce. Others will struggle to find

direction from the top. Some readers will be in roles that actively contribute

to strategy, while others will feel very disconnected from their

organization’s strategy insiders. Whatever your strategic situation and role,

if you are now inspired to innovate and make money from data, you need to

add discipline to your enthusiasm. If a top-down directive is lacking, then

you can connect to local priorities. A vision will help you avoid monetizing

data in random and uncoordinated ways, which produces convenient

outcomes, not optimal ones.

This chapter will help you appreciate where your organization is heading

(so you can head in the same direction). If your organization doesn’t have a

well-marked north star, the chapter will help you identify a data

monetization direction that might work in your context. Let’s begin with the

research.

Four Data Monetization Strategy Archetypes

Just as personas stand in for users in product design, archetypes will be

used in this chapter to represent four strategies (directions) you can take to

monetize data: operational optimization, customer focus, information

business, and future ready. The name of each strategy archetype sums up its

distinct data monetization vision; it conveys why you monetize. Each

archetype reflects different bottom-line financial priorities. For example, the

operational optimization strategy prioritizes cost efficiencies, whereas the

one called customer focus prioritizes finding ways to boost sales. The

following sections describe the four archetype strategies. You can think of

them as four quick sketches of distinct data monetization strategies.

Before jumping in, let’s go over where these archetypes came from. Back

in 2018, the authors surveyed 315 data leaders about their organizations’

data monetization capabilities, initiatives, and outcomes. The research team

clustered the respondents based on their answers to three questions about

how the value they were realizing from data monetization was distributed

among three categories of value realization: cost reduction, sales increase,

or direct revenues from information offerings. Four statistically robust

clusters emerged, and the researchers followed up with some respondents to

learn more.

Figure 7.2 shows, at the top, the distribution of financial returns (cost

reduction, sales increase, or direct revenues) reported by organizations in

each strategy archetype.3 The figure also includes three indices for each

archetype. The first index, the Value Realization Index, is a composite score

that reflects how much financial value the organization is realizing (relative

to its peers). The second index, the Competitive Strength Index, was

developed from five questions asking the respondents to rate the

competitive distinctiveness of their products and information solutions. The

third index, the Data Monetization Capability Index, shows the overall

capability score for each strategy (equivalent to adding up the five

individual capability scores in the capability assessment tool in the

appendix).

Figure 7.2

Key characteristics of four data monetization strategy archetypes

Notes: a The Value Realization Index is the sum of responses to three questions about operational

efficiencies; increased product prices, sales, or loyalty; and direct revenues from selling information

solutions, using a scale of 0–5, where 0 was “we do not do this” and 1–5 ranged from “very much

below the average of our peers” to “very much above the average of our peers.” b The Competitive

Strength Index is the sum of responses to five questions measuring the competitive distinctiveness of

wrapped products and information solutions, including whether they are first to market,

groundbreaking in the marketplace, profitable, superior to those of other organizations, and highly

valued by customers, using a scale of 1–5 ranging from “strongly disagree” to “strongly agree.” c The

Data Monetization Capability Index is the sum of the scores for the five capabilities. Each individual

capability score is the average of responses to three items asking about practices that build that

capability, using a scale of 0–5, where 0 is “we do not do this practice” and 1–5 ranges from “very

poorly developed” to “very well developed.”

Operational Optimization

The operational optimization strategy starts with a vision of internal

transformation. About a quarter (24 percent) of the organizations in the

study were categorized as having an operational optimization strategy.

These organizations relied more on stripping out costs for value realization

than organizations adopting any other strategy. In fact, 90 percent of the

data monetization value they realized was in the form of cost savings,

primarily from improving initiatives. They realized some value (7 percent

of total data monetization value realized) from sales lift by making

customer-facing improvements, mainly to processes that touched and

mattered to customers. These organizations realized a small amount of

value (3 percent) from selling (probably from selling data sets to industry

data aggregators). The gains from sales lift and selling were probably

incidental to the strategy.

Organizations pursuing the operational optimization strategy had the

lowest Value Realization Index of the four archetypes. This no doubt

reflects the challenges their leaders face in turning efficiency gains into

realized value—pushing money to the bottom line. They also have the

lowest Competitive Strength Index. In the past, organizations with this

strategy would not have expected their internal processes to be

competitively distinctive. After all, many organizations use the same off-

the-shelf technology and adopt similar management approaches. But today,

you can find a lot of entrepreneurial oomph in operations. Some

organizations are packaging operating data or transaction data into

“products” or “components” that can be easily accessed and reused

internally.4 As they begin to see opportunities to offer these data products to

external users as well, the competitiveness of these products becomes more

salient to them.

Operational optimization strategy adopters had the lowest Data

Monetization Capability Index of the four archetypes. They mainly invest in

capabilities necessary for understanding and shaping operations. They

generally design their organization to connect data and domain people in

key business processes and core functional areas. An example of an

organization that adopted this strategy is Microsoft, during the company’s

business model shift to cloud services, as described in chapter 3. It focused

mainly, but not exclusively, on improvement initiatives to reshape its

operations and create new processes and work tasks. People across the

company adopted practices like dashboarding. They accessed new data

assets from cloud platforms and used all five data-domain connection

approaches to develop and diffuse innovations.

Does operational optimization sound right for your organization? Here

are some points to consider:

• Don’t discount the big bottom-line impacts that can come from

adjusting and standardizing to new, better ways of work! This

strategy might be an apt choice if your organization has an

operating model that can scale process efficiencies across

franchises, production lines, or customer touchpoints.

• If your organization is transforming like Microsoft was, the pace

and expectations of an operational optimization strategy might

align well. This strategy aligns nicely with investments in new,

more contemporary technology and systems.

• Organizations still building foundational data monetization

capabilities might find a data monetization strategy that

prioritizes improving, like operational optimization, a safe way to

get started with internal-facing initiatives.

Customer Focus

The customer focus strategy starts with a vision of using data to delight

customers. The objective is to enhance the customer experience and serve

customers more efficiently. Organizations categorized as having a customer

focus strategy realized financial returns from a combination of stripping out

costs (60 percent of total value realized from data monetization) and sales

increases (30 percent of value realized). These organizations realize some

revenues (10 percent) from charging directly for wraps as well as from

selling data sets. Thirty percent of the sample fell into this cluster. Leaders

guided by this strategy invest in a mix of wrapping and improving

initiatives because better products and better processes are usually required

to provide better service to customers.

Organizations with a customer focus strategy had the second lowest

Value Realization Index of the four archetypes. Like those pursuing

operational optimization, these organizations are no doubt challenged to

realize value by cutting budgets. It’s no less challenging for them to realize

value by repricing products. However, because they realize value from both

improving and wrapping, they achieve more overall value than

organizations in the operational optimization cluster. Customer focus

organizations also had the second lowest Competitive Strength Index.

Perhaps they concentrate more of their wrapping efforts on keeping their

products from being commoditized rather than on beating the competition.

Over time, organizations that predominantly wrap learn that their wraps will

generate larger and more lasting value if they are first to market,

groundbreaking in the marketplace, profitable, superior to those of their

competitors, and highly valued by customers.

Leaders at organizations pursuing this strategy invest in capabilities that

support the delivery of customer-facing data and analytics at high levels of

service. The average Data Monetization Capability Index for organizations

with a customer focus strategy was higher than that of organizations with an

operational optimization strategy. Customer-facing initiatives raise the

capability bar.

Organizations with the customer focus strategy use multidisciplinary

teams to connect data people with their colleagues in product management,

sales, and marketing as well as with customers. These stakeholder

relationships help organizations develop wrap offerings that customers will

love and pay for. Chapter 4 described how multidisciplinary teams were

instrumental for PepsiCo in formulating win-win customer solutions for its

retail customers (and growing its joint sphere in the process). PepsiCo

leaders also made good use of embedded experts. The company’s Demand

Accelerator put data scientists and analysts full time in marketing, sales,

and advertising so the domain experts could learn data science techniques

and dream up clever ways to leverage PepsiCo’s extensive consumer data

assets.

Does customer focus feel right to you given your organization’s current

direction? Here are some points to consider:

• Organizations with an operating model that requires offering a

superior customer experience might find this a useful strategy.

• Organizations fighting to distinguish their products in competitive

markets might find that adding useful and engaging data-fueled

features and experiences might help their offerings stand out.

• Organizations that already have digital connections with customers

(through an app, a website, or even a product) might find this

strategy to be a good fit because they can leverage those

connections to experiment and iteratively hone product features

and experiences.

• Organizations eager to transition their business customer

relationships into partnerships—as PepsiCo did in chapter 4—

might be attracted to this strategy. As they deepen their business

customer interactions and knowledge sharing, they should

become better positioned to influence customer value creation.

Information Business

The information business strategy starts with a vision of using the

organization’s data assets to solve problems for other organizations (or

consumer markets). This strategy gets people to “think like the owner of an

information business” to find innovative ways to make money from data

assets. Organizations in the information business cluster focused mainly on

realizing value from selling information solutions (65 percent) while using

wrapping initiatives to sustain the sales of those solutions (15 percent).

Sixteen percent of organizations fell into this cluster (the smallest). These

organizations usually focus on reducing the cost of delivering information

solutions (10 percent), not stripping out costs by improving processes.

Organizations in the information business cluster had the highest Value

Realization Index and the highest Competitive Strength Index of the four

archetypes. Information solutions are typically high-margin offerings, and

realizing value comes naturally to the leaders of information businesses.

(The offerings have an explicit price tag, and customers pay it!) Leaders

guided by an information business strategy develop a business model

specific to selling. They quickly learn that to sustain high margins, solutions

must be competitively distinct.

As you know from chapter 5, the technical and managerial requirements

for selling are intense. Not surprisingly, organizations with this strategy also

have the highest Data Monetization Capability Index of the four archetypes.

They invest in practices, like emergent technology or sophisticated

analytical techniques, that reduce the cost and time of processing heaps of

data. These organizations are savvy to the core; many hire only “purple

people” and then offer training and other opportunities to keep them purple.

Healthcare IQ (from chapter 5) is an excellent example of a company that

adopted an information business strategy (after it built up its data assets) to

help hospitals cope with medical supply expenditures. Many of Healthcare

IQ’s staff were former hospital health spend analysts with strong analytical

abilities. Like many organizations pursuing the information business

strategy, Healthcare IQ drew heavily on connections that foster innovation,

like embedded experts and multifunctional teams. These connections helped

the company continuously develop and adapt its competitive solutions to

cope with market dynamism.

Does an information business strategy appeal to you (irrespective of

whether your organization would be considered an information business)?

Here are some points to consider:

• Is your organization good at bringing a new product to market or at

opening altogether new markets? Organizations seeking new

sources of revenue and the exciting momentum of a product

launch might find that a strategy focused on providing new

information solutions is the right choice.

• The information business strategy can work for an organization

with an incumbent business model if it is willing to start up a

separate unit to nurture and develop selling-related initiatives and

capabilities.

• The technical requirements and organizational commitment to

deliver information solutions are no joke; selling companies like

Healthcare IQ, TRIPBAM, and the other examples featured in

chapter 5 use relentless innovation and deep market

understanding to remain viable over time. This strategy should

come with a warning label to that effect.

Future Ready

Future-ready organizations are ambidextrous: they can significantly

improve their customers’ experience relative to competitors while

relentlessly cutting costs and simplifying their own operations.5

Organizations adopting the future-ready strategy want to realize value from

data in every way possible. A future-ready data monetization strategy

inspires people enterprise-wide to be agile, reuse capabilities, seek out

ecosystem opportunities, and exploit data assets. These organizations seek

to be efficient and customer oriented at the same time, so they become

skilled at constantly making trade-offs and adjustments.

Thirty percent of the organizations fell into this archetype. They captured

financial value through cost reduction (30 percent of total data monetization

value realized), sales increase (40 percent), and direct revenues (30

percent). To do this, they pursued value realization from initiatives of all

three types—improving, wrapping, and selling. Future ready is by far the

most difficult strategy to get right because of the need to be equally great at

all three approaches. The organizations most able to pursue a future-ready

strategy are digital organizations (or digitally transformed ones) and

organizations that are incredibly mature in their data monetization practices.

Organizations in the future-ready cluster had the second highest Value

Realization Index (behind only the information business strategy) and the

second highest Competitive Strength Index of the four archetypes.

These organizations also had the second highest Data Monetization

Capability Index. It is mainly the desire to produce information solutions

that drives the need for such strong data monetization capabilities. But

excellent capabilities have a positive spillover effect in that they allow the

organization to improve and wrap more effectively and efficiently. It’s

noteworthy that companies in the future-ready cluster had the highest score

on acceptable data use capability of all four archetypes. This capability

helps these organizations exploit data assets with confidence.

Leaders with future-ready aspirations work hard to establish a data

democracy that allows employees far and wide to engage in data

monetization initiatives relevant to their work. BBVA (from chapter 2)

pursued a future-ready strategy after it added wrapping to its mix of

pursuits. The company encouraged improving, wrapping, and selling, all of

which could draw on a growing collection of enterprise data monetization

capabilities and data assets. Like Microsoft, BBVA used all five kinds of

connections between data and domain experts to facilitate widespread

innovation and the diffusion of those innovations throughout the company.

Do you think your organization is primed to pursue a future-ready

strategy? Here are some points to consider:

• Google and other digitally born companies pursue the future-ready

strategy; their employees instinctively reach for data to solve any

problem. They improve, wrap, and sell all at the same time. So,

for example, developing a proposed website feature would be

expected to lower cost to serve and drive more sales while

contributing new data to the organization’s information solutions.

Organizations with this ambidextrous culture might find this

strategy to be a good fit.

• The future-ready strategy requires data monetization initiative

owners who can pursue goals that might normally be perceived as

competing (e.g., cost reduction and sales lift). Organizations with

process, product, and information solution owners who are great

at experimentation and making trade-offs might find this strategy

appealing.

• Organizations with robust, established governance processes and

effective ways to discuss and resolve conflicting goals might find

the future-ready strategy to be a good choice.

The Four Strategy Archetypes

MIT CISR researchers often compare top- and bottom-performing

organizations to understand what drives success. For example, the data

monetization strategy study showed that top performers in all four strategy

archetypes had monetization capabilities that were just about 1.5 times as

strong as those of bottom performers. Moreover, the top performers were

realizing about twice as much value from data monetization as were low

performers. Great capabilities are vital for achieving data monetization

returns.

Your data monetization strategy should reflect the aspirations of your

organization, not your industry. None of the four archetypes is aligned to

any industry. In the research sample, for example, a fairly even number of

financial services companies fell into each of the four archetypes (19

percent, 33 percent, 16 percent, and 32 percent, respectively), and there

were financial services companies among the top and bottom performers of

each archetype. That is, financial services firms can choose to compete in

many different ways (by transforming their operations, by being customer-

centric, by selling information solutions, or all three), and financial services

firms can execute their strategies very well—or poorly.

Smart organizations do not set their strategies in stone. A data

monetization strategy is a living, evolving plan that must be adapted over

time to accommodate shifts in market dynamics, technology advancements

(think digital), an organization’s evolving capabilities, and its overarching

business strategy. Thus, the data monetization strategy ideal for an

organization today might not be ideal a year from now; it will need to be

adjusted accordingly.

Questions to ask yourself

Which strategy archetype is the right choice for your organization now?

Which one might be the right choice in five years?

Using a Value-Effort Matrix to Select Data

Monetization Initiatives

Your data monetization strategy helps establish a vision. To make progress

on your strategy, you will need to make choices among the endless list of

opportunities—and investments—in front of you. So, the first test of a

strategy might be whether it helps you prioritize data monetization

opportunities. A simple technique for doing this (an approach your

organization might already use) is to array your options on a simple two-by-

two value-effort matrix, like the one depicted in figure 7.3, by the value

they offer and the effort they require.

Figure 7.3

A value-effort matrix

Here are two ways to refine your use of a value-effort matrix using what

you have learned about data monetization. First, define the vertical

dimension of the matrix, value, in terms of your organization’s data

monetization strategy (or the archetype strategy that most resonates with

you). Instead of arraying opportunities based on, say, projected ROI or

payback period, array them on the type of value your organization’s strategy

targets. If your organization’s strategy is operational optimization, use cost

savings as the value measure. If the strategy is customer focus, use a value

measure that combines sales increase and cost savings. (Just make sure, in

the end, that the complete set of approved initiatives does not target only

sales increases or only cost savings!) If you’re pursuing an information

business strategy, use revenues (direct sales and sales increases) for your

value measure. If your chosen strategy is future ready, use bottom-line

value realization.

Depending on your strategy, high-value opportunities (easy wins and

future possibilities) might solve a costly operational workaround, they

might relieve a customer pain point, or both. Low-value opportunities

(maybes and duds) are simply not in line with business priorities even if

they are effortless to pull off. Don’t be tempted by them.

Second, adapt how you estimate effort for the horizontal dimension of the

value-effort matrix. You now know that how much effort a data

monetization opportunity will require depends significantly on the state of

the data monetization capabilities available to your initiative teams. Data

monetization opportunities that require low effort are ones for which the

organization already has the necessary data assets and capabilities in place.

Here’s an example of a low-effort initiative: the required data assets can be

accessed from a cloud platform, the team has the right data science skills,

existing algorithms can be reused or adapted, customer needs are well

understood, and an acceptable data use policy and procedures are not only

in place but automated.

Initiatives for which capabilities are needed but not available can only

reasonably be tackled in the future. But there’s a silver lining: the more

initiatives in that upper right quadrant (high value but high effort) that need

the same data asset or data monetization capability, the more value there is

in investing directly in that reusable data resource.

Another factor that affects how much effort an initiative will require is

the state of your organization’s data democracy. What connections exist that

will help facilitate the initiatives under consideration? If the way you embed

a data expert in a domain is not already established, then a lot of effort will

be required to create new HR policies, work out incentive adjustments,

relocate the expert, and get people used to sharing knowledge. On the other

hand, if it’s just a matter of filling out a form and finding a desk, less effort

will be needed. Broadly speaking, less effort will be needed in those parts

of an organization where data is already democratized.

In sum, your top priority initiative should be one that will add a lot of

strategic value—easy money—to the bottom line and for which the data

resources and organizational connections needed are already available.

They are high strategic value and low data monetization effort.

As with many other estimation tools, this one is vulnerable to bias. This

can be mitigated by ranking initiatives along each axis using an evidence-

based approach or arriving at the location of an initiative along the axis by

consensus. The value-effort matrix is an easy-to-use tool for prioritizing

data monetization opportunities, especially if it is adapted based on what

you know about data monetization.

The important thing is to pursue data monetization thoughtfully, have a

good story to describe how things will unfold, and go after outcomes that

will be valued and achievable.

Time to Reflect

A data monetization strategy indicates how an organization plans to

generate its data monetization returns. It should include an organization-

level view of goals, prioritized opportunities, targeted capabilities, and an

ideal organizational design. The process of crafting a tailored data

monetization strategy is an excellent opportunity to establish a common

language regarding data across the organization. Here are the key points

from this chapter to keep in mind:

• It’s desirable to have a data monetization strategy that can be

linked to an organization’s strategy. How widespread is the

understanding of your organization’s business strategy

throughout the organization? To what extent do your data

monetization investments reflect where your organization is

heading?

• There are four data monetization strategies that organizations

commonly pursue. Which strategy archetype is the most

consistent with the data monetization strategy being pursued by

your organization? Is your current data monetization strategy

your organization’s best choice? Looking back or looking

forward, do you see this choice changing?

• Each data monetization strategy requires different levels of

capabilities. Please take the time to assess your organization’s

data monetization capabilities using the worksheet in the

appendix if you have not already done so. Will your capabilities

support your chosen strategy? What practices does your

organization need to adopt to have the capabilities it needs to

support your desired strategy?

• Organizations need to view their capabilities and connections in

light of the data monetization strategy they are pursuing. How

hard is it for champions of new initiatives to predict when the

capabilities they need will be available? How formalized are the

connections between data and domain experts at your

organization?

You now have a vision for how you want to generate your data

monetization returns, and you probably have the germ of a data

monetization strategy. In the next chapter, you will learn that it’s time to

make it your business to monetize data.

Monetizing Your Data

Everybody has this idea that there is value in data, and they want to tap into it—but

there is a lack of understanding of where to begin and how to get past the hype.

—Sean Cook, Pacific Life

When you head to work tomorrow, you might encounter a new data

buzzword, a fascinating new data technique, a sexy new tech platform, or a

colleague eager to share a shocking tweet about data. There’s always

something new about data to take in, something new that you need to

evaluate, debate, choose, or figure out. (There are also weighty concerns

outside the scope of this book that merit deep thought, like ethical AI and

consumer data privacy.) You now have a starting point from which to

evaluate whether something new about data is worth investigating further.

You can put news about a competitor innovation, an industry shift, or a new

privacy regulation in context. The data world is turbulent and

entrepreneurial, always changing. Now, you should feel better prepared to

get past the hype, as Sean Cook says above.

First, a very short recap of the foundational ideas presented in this book:

• Data monetization should be like every other day-to-day activity

you perform to do your job. It shouldn’t be seen as ambiguous or

inscrutable; it is simply the act of generating financial returns

from data assets. Organizations should expect an ROI from their

expenditures in data assets, data monetization capabilities, data-

related initiatives, and data-domain connections. Data needs to be

monetized so that inflows exceed outflows.

• Data monetization comes from improving, wrapping, and selling

initiatives. It requires five enterprise capabilities and thrives in

organizations replete with connections between data and domain

experts.

• A data monetization strategy communicates specific investment

choices. A strategy helps people head in the same direction and

contribute to the organization’s top priorities.

Armed with this understanding of data monetization, you can engage

more deeply with data-related issues and see fresh opportunities in

changing circumstances. Given a chance to automate a routine process, for

example, you might first ask your manager, “How are we going to cut out

the slack this will free up?” When your competitor begins to package its

physical product with a dashboard, you will probably ask, “What will their

customers have to do to create value from that dashboard? How much value

will they realize?” If a partner approaches you with an idea to cocreate a

new information solution, you might wonder, “Do they have the

capabilities?” And when a new privacy regulation is released, you might tap

your network of “purple people,” knowing that both data and domain

perspectives will help to make an informed plan as to what needs to change.

The book concludes with suggested next steps for you to take: (1)

evaluate the current state, (2) establish a way to track your progress, and (3)

make it your business to monetize data.

Evaluate the Current State

The book’s frameworks can help you take a snapshot of the current state of

data monetization at your organization or within your unit or team. That

snapshot can serve as your baseline. Then over time, you can measure

progress against it.

Because data monetization relies on data, first consider your

organization’s key data assets. Does your organization have data assets that

are a “single source of truth” about money, customers, employees, products,

patients, legal cases, projects, or any other subject matter that is important

to your organization? Are they accurate, complete, current, standardized,

searchable, and understandable? With answers to these questions in mind,

you can begin evaluating your capabilities, initiatives, and connections.

Figure 8.1 lists the big-picture questions to ask next.

Figure 8.1

Assessing the current state of data monetization

How well does your organization produce data assets that employees

can endlessly exploit? Reusable data assets result from monetization

capabilities, which come from your organization’s data monetization

practices. You can use the capability assessment worksheet in the appendix

to evaluate your data management, data platform, data science, customer

understanding, and acceptable data use capabilities. It will help you

understand whether your practices are likely to yield capabilities (and great

data assets) at all and where your capabilities are strongest and weakest.

Next, consider another run-through with the worksheet to assess which

practices are in place across the enterprise; get a feel for which of your

organization’s capabilities are “enterprise” capabilities. Those answers will

shed light on how well your data assets have been set up for reuse.

How much are data monetization efforts paying off? Most people find

it highly informative, if not fascinating, to dig into the question of whether

your data monetization efforts are paying off. Try to quantify the outcomes

of some of your organization’s recent data monetization initiatives: What

kinds of value and how much value have been created and realized from

improving, wrapping, and selling initiatives over the last three years? Did

the initiatives achieve the sort of bottom-line returns that were expected?

Were the right people held accountable for managing the risks and

outcomes of these initiatives?

How extensively has your organization seeded or formed a data

democracy? To identify the “purple-ness” of your organization (where

purple people can be found, where red and blue people are turning purple),

evaluate your organization’s data monetization connections: Of the five

kinds of connections—embedded experts, multidisciplinary teams, shared

services, social networks, advisory services—how many are being used in

your organization? Can multifunctional teams be formed quickly, or does it

take conspiring and cajoling? Are social networks being exploited to share

knowledge about data problems and solutions? Are domain experts

motivated to interact with and learn from data experts or not?

The objective of all this questioning is to get the big picture and food for

thought. Your responses might give you confidence that your organization

is on track or they might not. Just seeing how easy or hard it is to track

down the answers to these questions might help you spot areas that need

attention!

Establish a Way to Track Progress

You can’t manage what you can’t measure. As you move ahead, inspired to

act and drive change, you will need a way to track your data monetization

progress. You might be fortunate and work in an organization with formal

data monetization monitoring or you might not. Regardless of your

organization’s measurement culture, the frameworks will offer guidance.

Capabilities, initiatives, and connections need to be measured to some

degree. If your organization already has measures to draw on, use them. If

not, you will need to formulate a way to capture information about the

components.

To give you a bit of inspiration, let’s go back to BBVA. BBVA

established a way to track its data monetization progress when it created the

BBVA D&A subsidiary. The subsidiary needed to develop a formal way to

monitor its financial health because it was expected to sell information

solutions, self-fund its operations, and engage in financial transactions with

the bank (such as paying royalties for the use of BBVA data assets). As one

of her first moves, the leader of BBVA D&A developed a framework to

classify projects based on their economic impact. She used the framework

to evaluate what kinds of value the portfolio of D&A projects would create:

some projects mainly increased sales or market size, others generated

operational efficiencies, and some created nonfinancial value (such as

contributing to BBVA capabilities).

Business units were accountable for creating and measuring the kind of

value expected from each project they sponsored. A financial expert was

hired to serve as director of finance and operations. He was responsible for

managing D&A’s diverse project portfolio, and he helped the business unit

leaders create appropriate measurement methodologies and validate

initiative outcomes. He ensured that the value created by the projects was

realized for both the subsidiary and the bank.

BBVA also gave D&A accountability for building enterprise capabilities

and teaching every bank employee about data science. To monitor its

progress on these goals, BBVA D&A created a dashboard that tracked the

unit’s progress in data science capability building and talent development.

The dashboard captured metrics like the number of data sets migrated from

a local database to BBVA’s enterprise data platform, the number of times

existing algorithms were reused in new projects, and the number of BBVA

employees who attended data science training. It helped subsidiary leaders

identify what pockets of the bank were actively contributing to data

monetization progress; they encouraged those active pockets to continue

and tried to rouse other parts of the company from their inactivity.

Using this book’s frameworks, you can see that BBVA tracked initiatives,

capabilities, and connections. Its proprietary economic impact framework

and measurement methodologies captured whether and how initiatives were

paying off, which allowed the subsidiary leaders to ensure they invested in

a mix of project types and that the bank gained financially from data

monetization projects. Their dashboard reflected the strength of BBVA’s

capabilities and data democracy.

If you, too, need to craft a way to track progress, do not feel

overwhelmed. There are two measurement principles to keep in mind. First,

measure in a way that is not overly complicated or costly. The cost of

measurement can’t be greater than the value of having the measurement in

hand. Ideally, organizations should measure just enough to sustain

organizational commitment over time. What is “just enough” will depend

on the organization’s specific needs. If the investment in a process

improvement is small, it might be enough to measure local efficiencies and

local cost reductions or slack reallocations. If the investment is large, it

might be important to surface efficiencies in downstream processes and any

related slack reduction or to measure product sales lift that can be traced to

the process improvement. Just be sure to say how you will verify that the

money reaches the bottom line.

Second, measure in a way that will be credible to the people in your

organization. Some organizations are wedded to hard evidence. They need

in-depth business cases and post-audits of every investment. Other

organizations need plausible evidence but are satisfied with anecdotes and

back-of-the-envelope extrapolations. Still others need the ongoing

production of evidence, generated by instrumentation and formalized

monitoring, for value management. Only you know what builds and

sustains commitment at your organization.

Make It Your Business to Monetize Data

This book has said at every turn that a lot of people in an organization need

to be involved in data monetization. In fact, it argues that data monetization

requires everybody. Process, product, and information solution owners must

take accountability for creating value from an organization’s data assets.

Experts need to learn from each other and share knowledge, employees

must act on insights and pursue innovations, and leaders have a duty to fund

and support the effort.

Even though data monetization takes everybody, a single person (or just a

few) can make a difference. The BBVA data monetization journey began

with four innovators who went on a sabbatical at MIT to learn about selling

information solutions. PepsiCo’s Demand Accelerator might never have

received funding had one seasoned and trusted sales leader not had the ear

of top leaders, so he could help them see the connection between analytics

and granular growth. Finally, many of the solutions and wraps that fueled

Healthcare IQ’s growth were proposed and championed by one customer

account rep who sensed a change in customer needs and helped the

company adapt its offerings and fill the need.

Now it’s your turn. Right now, your organization needs people like you to

get things started or keep things going. Look for opportunities to innovate

with data: to improve work, enhance a product, or design an information

solution. There is no better way to develop an intimate awareness of the

maturity of your data monetization resources and the challenges of pushing

money to the bottom line than to be part of an improving, wrapping, or

selling initiative team.

So start or join an initiative team. You can begin anywhere in the value-

creation process (data-insight-action), using a work challenge that you

currently face. Get some data: is there an open data set (there are over three

hundred thousand open data sets on data.gov) that could help you resolve

your challenge? Maybe you need more insight: could you use more

sophisticated number crunching? Maybe you need to standardize how you

take action: would automation help? Only through doing will you truly

learn how to monetize data.

Let’s say you decide to tackle one of these initiative ideas. Putting

together a team with all the expertise needed to launch, execute, and close

out the initiative will tell you everything you need to know about your

organization’s data-domain connections. After you get your team together,

you will need to round up the necessary data monetization capabilities. To

find capabilities, you’ll look for places where data monetization practices

are in place; the link between practices and capabilities will become crystal

clear to you. Working on your initiative, it will also become obvious why

all five capabilities are needed and why it’s better if capabilities are

enterprise capabilities.

Assuming the initiative is a smashing success, your next learning journey

will be turning the value you created into money. Your organization might

have a formal process to do this, or you may need to work with many

people to figure out how to make the organizational adjustments that will

push money to the bottom line.

When you actively engage in data monetization, you learn and you help

your organization learn. Your engagement powers the data monetization

flywheel, creating momentum that initiates a positive reinforcing cycle:

more data assets leading to more use, leading to more value, leading to

more data assets, leading to more use, and so on. Imagine this happening

across the organization as people everywhere make it their business to

monetize data.

This is why data is everybody’s business.

Appendix: The Capability Assessment Worksheet

The capability assessment worksheet in this appendix (table A.1) can be

used to assess the data monetization capabilities of your organization and

their levels (as in figure 2.3). It can also be used to calculate your

organization’s Data Monetization Capability Index (as in figure 7.2).

Table A.1

The capability assessment worksheet

Capability Typical practices

Your

score

(0–5)

Your

capability

level

(F, I, or

Capability Typical practices

Your

score

(0–5)

Your

capability

level

(F, I, or

Data management:

To build a data

management

capability,

organizations engage

in practices that turn

data into accurate,

integrated, and curated

data assets.

Foundational: Master

data

Practices that produce

reusable data assets

include establishing

automated data-quality

processes, identifying

data sources and flows

that describe core

business activities or key

entities like customer and

product, creating

standard definitions of

priority organizational

data fields, and

establishing metadata for

those data fields.

Intermediate:

Integrated data

Practices that allow data

to be integrated from

both internal and external

sources include mapping

and harmonizing data

sources and

standardizing, matching,

and joining data fields.

Capability Typical practices

Your

score

(0–5)

Your

capability

level

(F, I, or

Advanced: Curated

data

Organizations use

taxonomy and ontology

to curate their data.

These practices involve

analyzing data and its

relationships, depicting

data and its relationships

in a way that is

accessible and

meaningful to users, and

maintaining that

depiction over time.

These practices make it

possible to augment the

organization’s data assets

with data assets from

external sources or with

data assets created as a

byproduct of the

development of AI

models.

Capability Typical practices

Your

score

(0–5)

Your

capability

level

(F, I, or

Data platform: To

build a data platform

capability,

organizations engage

in practices that allow

them to draw on cloud,

open source, and

advanced database

technologies to

produce software and

hardware

configurations that

satisfy their data

processing,

management, and

delivery needs.

Foundational:

Advanced tech

The adoption of cloud-

native technologies is an

example of a data

platform practice.

Modern database

management tools

include products that

leverage leading-edge

techniques for data

compression, storage,

optimization, and

movement.

Intermediate: Internal

access

The use of APIs to offer

data and analytics

services internally is a

practice that eases access

to raw data or data assets

from any system.

Capability Typical practices

Your

score

(0–5)

Your

capability

level

(F, I, or

Advanced: External

access

APIs can also be used to

make an organization’s

raw data or data assets

available to external

channels, partners, and

customers. Providing

APIs to stakeholders

outside the organization

requires adopting

practices for certifying

external users and

tracking their platform

activity.

Capability Typical practices

Your

score

(0–5)

Your

capability

level

(F, I, or

Data science: To build

a data science

capability,

organizations engage

in practices that

advance their ability to

use data science

techniques and

thinking. They hire

new talent and upskill

and develop existing

employees. They

invest in tools and

methods that support

data science work so

that data science tasks

can be appropriately

managed and scaled.

Foundational:

Reporting

Practices that foster the

use of dashboards and

reporting include

standardizing data

presentation tools and

designating which data

assets will be regarded as

the “single source of

truth” for process

outcomes or business

results. They include

educating employees

about data storytelling

and evidence-based

decision-making.

Capability Typical practices

Your

score

(0–5)

Your

capability

level

(F, I, or

Intermediate: Statistics

Practices that promote

the use of math and

statistics include

selecting analytics tools,

hiring people with

sophisticated

mathematical and

statistical knowledge,

and establishing data

science support units.

They include teaching

probability, statistics, and

skills that increase the

usability of analytics

tools and techniques.

Capability Typical practices

Your

score

(0–5)

Your

capability

level

(F, I, or

Advanced: Machine

learning

To promote the use of

advanced analytics

techniques such as

machine learning, natural

language processing, or

image processing,

organizations engage in

feature engineering,

model training, and

model management.

They use AI explanation

practices that ensure AI

models are value

generating, compliant,

representative, and

reliable.1

Capability Typical practices

Your

score

(0–5)

Your

capability

level

(F, I, or

Customer

understanding: To

build a customer

understanding

capability,

organizations connect

with customers to

collect data about them

—demographics,

sentiments, context,

usage,

Foundational:

Sensemaking

Listening to customers

and making sense of their

needs is an example of a

foundational customer

understanding practice.

Customer-facing

employees can help

organizations identify

important customer needs

by sharing ideas via

“suggestion boxes” or

crowd-sourced

innovation events. These

employees can also

participate in agile or

cross-functional teams

tasked with mapping

customer journeys or

designing new products

and processes.

Capability Typical practices

Your

score

(0–5)

Your

capability

level

(F, I, or

and desires—from

which they extract

analytical insights

about core and latent

customer needs.

Intermediate:

Cocreation

Engaging customers in

the cocreation of new

products or new

processes requires

practices for identifying

the appropriate customer,

establishing the terms of

customer engagement,

and making good use of

customer time.

Advanced:

Experimentation

Common practices for

testing ideas with

customers include

hypothesis testing

(observing customer

behavior to see if it

conforms to

expectations) and the use

of A/B testing (using

randomized

experimentation with two

variants, A and B).

Capability Typical practices

Your

score

(0–5)

Your

capability

level

(F, I, or

Acceptable data use:

To build an acceptable

data use capability,

organizations engage

in practices that allow

them to effectively

address regulatory and

ethical concerns

regarding data asset

use by and about

employees, partners,

and customers.

Organizations draw on

this capability to

mitigate the risk of

using data assets

inaccurately,

undesirably, or in ways

that are not

contractually or legally

allowable.

Foundational: Internal

oversight

Practices that ensure

acceptable use of data by

employees usually begin

with establishing data

ownership; training

employees about laws,

regulations, and

organizational policy;

setting up data access

approval processes; and

auditing employee data

access.

Intermediate: External

oversight

Practices that ensure the

appropriate use of data

assets by partners begin

with establishing clear

agreements about

appropriate use with

partners and end with

auditing partner use of

data assets.

Capability Typical practices

Your

score

(0–5)

Your

capability

level

(F, I, or

Advanced: Automation

Practices that allow

customers to self-manage

their data begin with

establishing policies

regarding customer

control of data. These

policies are then

implemented both by

communicating the

policies to customers and

facilitating customer

control through

automation. Automating

practices also helps

organizations scale

internal and external

oversight activities.

Here’s how to use the worksheet. If your organization is large, with many

business units, you may wish to focus your assessment on the capabilities of

a particular business unit. If your focal business unit receives data-related

services from a shared services or corporate IT unit, include the practices of

that unit in your assessment since that unit’s data monetization capabilities

are made available to you.

First, score your chosen business unit on its level of adoption and use of

each of the three practices within each capability. Use a scale of 0–5 (0 =

we do not have this practice, 1 = very poorly developed, 2 = somewhat

poorly developed, 3 = of average development, 4 = somewhat well

developed, 5 = very well developed).

To assign a capability level to each asset, refer to your scores. Select the

level (foundational, intermediate, or advanced) with the highest score. If

you do not have a score of 3 or above for the foundational-level practices

for a particular capability, that capability is not yet established. If two levels

have the same score, select the higher level. For example, if the

foundational and intermediate practices are somewhat well developed, and

you gave those practices a score of 4 but some advanced practices have

been adopted but are poorly developed, scoring 1, then the level of that

capability would be intermediate. Note that the practices that build data

monetization capabilities are sequential, meaning that practice scores are

typically highest at the foundational level and lowest at the advanced level.

You can compare your scores to those provided by the 315 executives

responding to our 2018 survey,2 which can be found in the second table

(table A.2). Note that survey respondents’ capabilities were, on average, at

a foundational level. To calculate your Data Monetization Capability Index,

first average the three scores for each capability (the three levels). For

example, if for the data management capability your foundational practices

score was 4, your intermediate practices score was 4, and your advanced

practices score was 1, then the average score for this capability would be 3

[(4 + 4 + 1)/3]. Then, sum up the five capability scores. The scores for this

index range from 0 to 15.

Table A.2

Scores from 315 MIT CISR survey participants

Capability Typical practices

Average

scores

score

(0–5)

Average

capability

level

(F, I, or A)

Capability Typical practices

Average

scores

score

(0–5)

Average

capability

level

(F, I, or A)

Data

management

Foundational: Master

data

Practices that produce

reusable data assets include

establishing automated

data-quality processes,

identifying data sources

and flows that describe

core business activities or

key entities like customer

and product, creating

standard definitions of

priority organizational data

fields, and establishing

metadata for those data

fields.

3.2 Foundational

Intermediate: Integrated

data

Practices that allow data to

be integrated from both

internal and external

sources include mapping

and harmonizing data

sources and standardizing,

matching, and joining data

fields.

2.9

Capability Typical practices

Average

scores

score

(0–5)

Average

capability

level

(F, I, or A)

Advanced: Curated data

Organizations use

taxonomy and ontology to

curate their data. These

practices involve analyzing

data and its relationships,

depicting data and its

relationships in a way that

is accessible and

meaningful to users, and

maintaining that depiction

over time. These practices

make it possible to

augment the organization’s

data assets with data assets

from external sources or

with data assets created as a

byproduct of the

development of AI models.

2.5

Capability Typical practices

Average

scores

score

(0–5)

Average

capability

level

(F, I, or A)

Data platform Foundational: Advanced

tech

The adoption of cloud-

native technologies is an

example of a data platform

practice. Modern database

management tools include

products that leverage

leading-edge techniques for

data compression, storage,

optimization, and

movement.

3.1 Intermediate

Intermediate: Internal

access

The use of APIs to offer

data and analytics services

internally is a practice that

eases access to raw data or

data assets from any

system.

3.0

Capability Typical practices

Average

scores

score

(0–5)

Average

capability

level

(F, I, or A)

Advanced: External

access

APIs can also be used to

make an organization’s raw

data or data assets available

to external channels,

partners, and customers.

Providing APIs to

stakeholders outside the

organization requires

adopting practices for

certifying external users

and tracking their platform

activity.

2.3

Data science Foundational: Reporting

Practices that foster the use

of dashboards and reporting

include standardizing data

presentation tools and

designating which data

assets will be regarded as

the “single source of truth”

for process outcomes or

business results. They

include educating

employees about data

storytelling and evidence-

based decision-making.

3.6 Intermediate

Capability Typical practices

Average

scores

score

(0–5)

Average

capability

level

(F, I, or A)

Intermediate: Statistics

Practices that promote the

use of math and statistics

include selecting analytics

tools, hiring people with

sophisticated mathematical

and statistical knowledge,

and establishing data

science support units. They

include teaching

probability, statistics, and

skills that increase the

usability of analytics tools

and techniques.

3.1

Capability Typical practices

Average

scores

score

(0–5)

Average

capability

level

(F, I, or A)

Advanced: Machine

learning

To promote the use of

advanced analytics

techniques such as machine

learning, natural language

processing, or image

processing, organizations

engage in feature

engineering, model

training, and model

management. They use AI

explanation practices that

ensure AI models are value

generating, compliant,

representative, and reliable.

2.2

Capability Typical practices

Average

scores

score

(0–5)

Average

capability

level

(F, I, or A)

Customer

understanding

Foundational:

Sensemaking

Listening to customers and

making sense of their needs

is an example of a

foundational customer

understanding practice.

Customer-facing

employees can help

organizations identify

important customer needs

by sharing ideas via

“suggestion boxes” or

crowd-sourced innovation

events. These employees

can also participate in agile

or cross-functional teams

tasked with mapping

customer journeys or

designing new products and

processes.

3.1 Foundational

Capability Typical practices

Average

scores

score

(0–5)

Average

capability

level

(F, I, or A)

Intermediate: Cocreation

Engaging customers in the

cocreation of new products

or new processes requires

practices for identifying the

appropriate customer,

establishing the terms of

customer engagement, and

making good use of

customer time.

2.9

Advanced:

Experimentation

Common practices for

testing ideas with

customers include

hypothesis testing

(observing customer

behavior to see if it

conforms to expectations)

and the use of A/B testing

(using randomized

experimentation with two

variants, A and B).

2.8

Capability Typical practices

Average

scores

score

(0–5)

Average

capability

level

(F, I, or A)

Acceptable

data use

Foundational: Internal

oversight

Practices that ensure

acceptable use of data by

employees usually begin

with establishing data

ownership; training

employees about laws,

regulations, and

organizational policy;

setting up data access

approval processes; and

auditing employee data

access.

3.0 Foundational

Intermediate: External

oversight

Practices that ensure

appropriate use of data

assets by partners begin

with establishing clear

agreements about

appropriate use with

partners and end with

auditing partner use of data

assets.

2.8

Capability Typical practices

Average

scores

score

(0–5)

Average

capability

level

(F, I, or A)

Advanced: Automation

Practices that allow

customers to self-manage

their data begin with

establishing policies

regarding customer control

of data. These policies are

then implemented both by

communicating the policies

to customers and

facilitating customer

control through automation.

Automating practices also

helps organizations scale

internal and external

oversight activities.

2.3

Note: The 315 survey respondents included executives from organizations of all sizes, with 44

percent having more than US$3 billion in annual revenues in 2017 and 32 percent having revenues

under US$500 million. The majority of the executives’ organizations were for profit; 42 percent were

publicly traded, and 18 percent were nonprofit or governmental organizations. The organizations

operated worldwide, with 79 percent having some operations in North America. The organizations

competed in a range of industries; 39 percent competed in the industry categories of financial

services/banking, manufacturing, and professional services.

Source: Barbara H. Wixom, “Data Monetization: Generating Financial Returns from Data and

Analytics—Summary of Survey Findings,” Working Paper No. 437, MIT Sloan Center for

Information Systems Research, April 18, 2019,

https://cisr.mit.edu/publicationMIT_CISRwp437_DataMonetizationSurveyReport_Wixom (accessed

January 17, 2023).

Acknowledgments

We are grateful to the MIT Press team for their commitment and expertise.

For their comments and their time, we are grateful to the anonymous

reviewers and to our friends and colleagues who read early versions of the

manuscript: Gregg Gullickson, Gigi Kelly, Ann Murphy, and Gary

Scholten. Collaborating with our talented graphic designer Alli Torban was

inspiring. Alli is patient, professional, intuitive, and creative. Alli’s design

process influenced our writing for the better, and her delightful graphics

will help our ideas stick.

People across MIT helped shape and strengthen our ideas. Thanks to our

MIT CISR colleagues for providing encouragement and incisive feedback:

Isobela Byerly-Chapman, Kristine Dery, Jed Diamond, Margherita DiPinto,

Chris Foglia, Nils Fonstad, Amber Franey, Dorothea Gray, Nick van der

Meulen, Cheryl Miller, Ina Sebastian, Jeanne Ross, Aman Shah, Austin Van

Groningen, Peter Weill, and Stephanie Woerner. Thank you to all our

colleagues at the MIT Sloan School of Management, the Sloan

Management Review, and the MIT Sloan research centers, with special

appreciation to Dean Schmittlein, Michael Cusumano, Elizabeth Heichler,

Abby Lundberg, and Wanda Orlikowski.

We appreciate the expertise and encouragement of the MIT Sloan

Executive Education team who guided our distillation of three decades of

data monetization research into a six-week, online data monetization

strategy course: Isabella DiMambro, Christine Gonzalez, Peter Hirst, Paul

McDonagh-Smith, Meg Regan, and the GetSmarter Team (Cara Dewar,

Andre Grobler, Pamela MacQuilkan, and John Ruzicka). These educational

designers raised questions that helped us hone our content and improve its

delivery. Also, we appreciate our executive education colleagues—and our

colleagues in the MIT Industrial Liaison Program—for introducing us to

curious global executives who actively engage with our material and help

us appreciate why and how data monetization matters to leaders of all

kinds.

This book reflects decades of research by a village of academic

collaborators. We greatly appreciate Ida Someh’s helping us understand

how organizations become data democracies and what it takes to scale AI.

Ida is a qualitative research guru who culled critical insights from MIT

CISR interviews and case studies (including at BBVA, General Electric, and

Microsoft). Thank you, Ida, for your intellectual contributions, as well as

your positive attitude and your friendship. Special thanks to Ronny Schüritz

and Killian Farrell for helping us understand how product managers use

data analytics to enhance their offerings. Ronny and Killian are gifted data

scientists who helped us develop and advance the concepts of data

wrapping and data monetization capabilities. We are grateful to Gabriele

Piccoli and Joaquin Rodriguez, who helped us examine data monetization

using their theoretical lens of digital resources. Their collaboration has

produced exciting concepts like digital data assets, actioned analytics, and

data liquidity, as well as cases such as Fidelity and TRIPBAM (the latter

along with Federico Pigni). Over the years, many other collaborators have

generously contributed their subject area expertise to assist us with specific

project needs; thank you to Anne Buff, Justin Cassey, Wynne Chin, Tom

Davenport, Tamara Dull, Dale Goodhue, Robert Gregory, Rajiv Kohli,

Dorothy Leidner, M. Lynne Markus, Anne Quaadgras, Paul Tallon, Peter

Todd, Olgerta Tona, Hugh Watson, Rick Watson, and Angela Zutavern.

It is essential for academic work to reach—and be informed by—

practitioner audiences. Thank you to the thousands of interviewees, case

study participants, and survey respondents who contributed to this research

over three decades. Special thanks to those leaders who allowed us to

feature them in our research: Linda Abraham, Magid Abraham, Scott Albin,

Elena Alfaro, Juan Murillo Arias, Sarmila Basu, Julie Batch, Tom Bayer,

Marco Bressan, Mike Brown, Peter Campbell, Tom Centlivre, Michael

Cleavinger, Reid Colson, Sean Cook, Jeff Dale, Norm Dobiesz, Jim

DuBois, Gian Fulgoni, Danny Gilligan, Enrique Hambleton, Sue Hanson,

Scott Heintzeman, Kathy Hollenhorst, Brandon Hootman, Randy Hurst,

Gregg Jankowski, Vince Jeffs, Robert Jones, Nir Kaldero, David Lamond,

Dick LeFave, Mike McClellan, Shamim Mohammad, Detlef Nauck, Sandra

Neale, Rob Phillips, Michelle Pinheiro, Vijay Raghavan, Vijay Ravi, Jeevan

Rebba, Michael Relich, Anne Marie Reynolds, Steve Reynolds, Linda

Richardson, Rajeev Ronanki, Martha Roos, Marek Rucinski, Laura Sager,

Kiki Sanchez, Mary Schapiro, Mihir Shah, Marcus Shipley, John Shomaker,

Danny Slingerland, Tim Smith, Chris Soong, Scott Stephenson, Don Stoller,

Jeff Stovall, Jeff Swearingen, Rim Tehraoui, Omid Toloui, Robert Welborn,

David Wright, Jacky Wright, Bruce Yen, and Ying Yang.

We are beholden to the global leaders who fund our work at MIT CISR

and participate in our research consortium. The current list of sponsors and

patrons is available here: https://cisr.mit.edu/content/mit-cisr-members.

Special thanks to the liaisons who provided extraordinary enthusiasm and

support for our research: Nuno Barboza, Duke Bevard, Chris Blatchly, Deb

Cassidy, Stijn Christiaens, Karen Clarke, Vittorio Cretella, Bernard

Gavgani, David Hackshall, Alexander Haneng, Craig Hopkins, Dirk van der

Horst, Naomi Jackson, Jeff Johnson, Carolyn Cameron-Kirksmith, Michelle

Mahoney, Jaime Montemayor, Mark Meyer, Robert Oh, Patrick O’Rourke,

Kal Ruberg, Tek Singh, Ivan Skerl, David Starmer, Jim Swanson, Bernardo

Tavares, Donna Vinci, Steve Whittaker, Pui Chi Wong, and Edgar van

Zoelen.

Finally, this book would not have been possible without extraordinary

contributions by the MIT CISR Data Board. Since 2015, several hundred

data and analytics leaders have piloted and perfected surveys, cleared

schedules for research interviews and virtual discussions, debated findings,

and shared struggles and wins. These lifelong learners are curious,

encouraging, and enthusiastic partners who elevate our research and help

move the field forward. We are grateful to every one of you. Thanks to

those who helped establish the board and create a spirited community:

David Abrahams, Jennifer Agnes, Laki Ahmed, Daniel Bachmann, Melanie

Bell, Aurelie Bergugnat, Michael Blumberg, Gustavo Botelho de Souza,

Gavin Burrows, Jonathan Carr, Licio Carvalho, Fiona Carver, Rafael

Cavalcanti, Harj Chand, Krishna Cheriath, Marlo Cobb, Glenn Cogar, Scott

Cooper, Tony Cossa, Glenda Crisp, José Luis Dávila, Regine Deleu, Steve

DelVecchio, Jeff DeWolf, Tej Dhawan, David Dittmann, Andrew Dobson,

Brad Fedosoff, Mavis Girlinghouse, Paul Grant, Ritesh Gupta, Sofia

Hagström, Richard Hines, Dan Holohan, Ali Kettani, Jane King, Jim

Kinzie, Joe Kleinhenz, Kate Kolich, Ram Kumar, David Lamond, Jorge

Llerena, Ling Ling Lo, Gary Lotts, Andre Luckow, Esther Málaga, Jurgen

Meerschaege, Malavika Melkote, Didem Michenet, Abhishek Mittal,

Fredrik Ohlsson, Doug Orr, Macaire Pace, Nanda Padayachee, Ajay

Padhye, Tom Pagano, Doraivelu Palanivelu, Diogo Picco, Kala

Ramaswamy, Perry Rotella, Riaan Rottier, Rob Samuel, Sai Seethala, Tom

Serven, Amy Shi-Nash, David Short, Fausto Sosa, Jim Tanner, Gilberto

Flórez Tella, Simon Thompson, Mike Trenkle, David Vaz, Kate Wei, Greg

Williams, Janine Woodside, Brett Woolley, Floyd Yager, Kelley Yohe,

Brian Zacharias, and Jenny van Zyp.

A Personal Note from Barb

This book is the result of thirty years of academic research regarding a

single question inspired by my doctoral work: How do organizations

generate value from data? Arguably, this book is my dissertation finale!

Thank you to my coauthors Cynthia Beath (known as “Boo”) and Leslie

Owens for helping me create a book I hope will help people reach their data

dreams. I appreciate your complementary contributions, your patience, and

your friendship—all of which made our collaboration a life gift.

Three influential mentors kept me on course over three decades: Hugh

Watson, Ryan Nelson, and Cynthia Beath. Hugh—you inspired me to love

data, appreciate practice, and make a difference in the field. Ryan—you

advised me through big transitions, reminded me that fun matters, and

encouraged me to reach for the stars. Boo—you saw my potential, built my

confidence, and helped me touch those stars. Hugh, Ryan, and Boo, you

mean the world to me.

Countless people influenced and inspired my work. I am grateful to my

academic and practice friends at AIS, SIG-DSA, SIM, TDWI, TUN, and

UVA’s McIntire School. Special thanks to Susan Baskin, Steve Cooper,

Scott Day, Alan Dennis, Howard Dresner, Jill Dyché, Wayne Eckerson, Dan

Elron, Scott Gnau, Jane Griffin, Richard Hackathorn, Martin Holland, Cindi

Howson, Cyndy Huddleston, Claudia Imhoff, Bill Inmon, Lakshmi Iyer,

Adelaide King, Mary Lacity, Doug Laney, Evan Levy, Shawn Rogers,

Anne-Marie Smith, Catherine Szpindor, Rhian Thompson, Rich Wang,

Madeline Weiss, and Robert Winter.

Thank you to my family and personal friends for your love and support—

and your patience and understanding—as I lean into my professional

pursuits. You infuse my life with positive energy and make me a stronger

and better person.

Thank you to my husband, Chris, and my daughters, Haley and Hannah. I

am humbled by your unwavering love, encouragement, and humor during

this book journey and throughout my professional career. Your positivity

and zest for life inspire me every day. Chris, Haley, and Hannah: my heart

bursts with love for you.

A Personal Note from Cynthia (a.k.a. “Boo”)

When Jeanne W. Ross, Martin Mocker, and I sought feedback on early

drafts of our book Designed for Digital (MIT Press, 2019), the most

consistent piece of feedback we got was, “WHAT ABOUT DATA?” Our

response was, “That’s going to take a whole other book!” This is that book.

I thank Barb and Leslie for the invitation to join this team, for their patience

with me, and for the many, many laughs along the way. This has been an

incredible journey.

Some additional thanks are in order.

First and foremost, I thank my husband, Denny McCoy. Thank you,

Denny, for empathizing with my creative challenges and not trying to fix

them. Thank you for helping me face up to the blank page one day before

the last possible moment to begin. Thank you for teaching me to see that

sometimes a piece of creative work is actually “finished.” Thank you for

teaching me that it is possible to be creative and disciplined at the same

time. (I’m sorry I have not yet grasped the “creative and tidy at the same

time” lesson.) Thank you for all the hugs and cups of coffee.

Second, I thank Burt Swanson for getting me started on this fabulous

journey of research and discovery. Burt taught me to be guided in my

research by relevance to practice, and that has kept my enthusiasm for

research high throughout my career. He also showed me that research took a

lot of pure perseverance, more rewriting than seems possible, and way more

thinking than a human can tolerate. I thank him for all of that.

Third, I thank my much-missed little dog, Dolly Mama, for listening

uncritically to my ranting, for being patient with me when I worked too

long, and for always being up for a walk to breathe out the ideas that

weren’t working and breathe in some new ones.

Finally, many thanks to the universe for giving me everything I could

ever want.

A Personal Note from Leslie

Thank you to Barb and Boo for inviting me into this fun and rewarding

experience. I appreciate your humor, kindness, and can-do spirit.

Although this book is primarily about data, it’s also about people. As I

turn fifty this year, I am counting my blessings. I am lucky to have close

friends from various ages and stages of my life: childhood, college, work,

neighbors, and more. My mentors include Pauline Cochrane, Joe Coffey,

Win Lenihan, Jeff Lyons, Stephen Powers, and Rich Strle. I am so grateful

to all of you for carrying me through difficult times and helping me see and

celebrate the joys and opportunities that come my way.

Thank you to my family: Mom, Dad, Adrienne, Colleen, George, Scott,

Kelly, Erik, Graham, and Nick. My mom was a sensitive and smart

entrepreneur who gracefully balanced home and career at a time when there

were few examples out there. My dad was tenderhearted; he gave me

support and confidence. My husband, Erik, and our son, Graham: you are

the most precious people in my life. Thank you for giving me pep talks and

perspective. You set the example for hard work and goodwill that I try to

follow. I love you both with my whole heart.

Notes

Introduction

1. Miriam Daniel, “Immersive View Coming Soon to Maps—Plus More Updates,” The Keyword,

May 11, 2022, https://blog.google/products/maps/three-maps-updates-io-2022 (accessed August

30, 2022).

2. “Alphabet Q2 2022 Earnings Call Transcript,” Alphabet Investor Relations, July 26, 2022,

https://abc.xyz/investor/static/pdf/2022_Q2_Earnings_Transcript.pdf (accessed August 30, 2022).

3. Barbara H. Wixom and Gabriele Piccoli, “Build Data Liquidity to Accelerate Data Monetization,”

MIT Sloan Center for Information Systems Research, Research Briefing, vol. XXI, no. 5, May 20,

2021, https://cisr.mit.edu/publication/2021_0501_DataLiquidity_WixomPiccoli (accessed January

17, 2023).

4. Barbara H. Wixom, Thilini Ariyachandra, Michael Goul, Paul Gray, Uday Kulkarni, and Gloria

Phillips-Wren, “The Current State of Business Intelligence in Academia,” Communications of the

AIS 29, no. 1 (2011), http://aisel.aisnet.org/cais/vol29/iss1/16 (accessed January 17, 2023).

5. Mark Mosley and Michael Brackett, eds., The DAMA Guide to the Data Management Body of

Knowledge (DAMA-DMBOK Guide) (Bradley Beach, NJ: Technics Publications 2009).

Chapter 1
1.  Jeanne  W.  Ross,  Cynthia  M.  Beath,  and  R.  Ryan  Nelson,  “Redesigning  CarMax  to  Deliver  an
Omni-Channel  Customer  Experience,”  Working  Paper  No.  442,  MIT  Sloan  Center  for
Information  Systems  Research,  June  18,  2020,
https://cisr.mit.edu/publication/MIT_CISRwp442_CarMax_RossBeathNelson  (accessed  January
17, 2023).
2.  Ida  A.  Someh  and  Barbara  H.  Wixom,  “Microsoft  Turns  to  Data  to  Drive  Business  Success,”
Working  Paper  No.  419,  MIT  Sloan  Center  for  Information  Systems  Research,  July  28,  2017,
https://cisr.mit.edu/publication/MIT_CISRwp419_MicrosoftDataServices_SomehWixom
(accessed January 17, 2023).
3.  “BBVA,  an  Overall  Digital  Experience  Leader  Five  Years  in  a  Row,  According  to  ‘European
Mobile  Banking  Apps,  Q3  2021,’”  Banco  Bilbao  Vizcaya  Argentaria,  February  11,  2022,
https://www.bbva.com/en/bbva-an-overall-digital-experience-leader-five-year-in-a-row-
according-to-european-mobile-banking-apps-q3-2021/ (accessed August 30, 2022).
4. Barbara H. Wixom, “PepsiCo Unlocks Granular Growth Using a Data-Driven Understanding of
Shoppers,”  Working  Paper  No.  439,  MIT  Sloan  Center  for  Information  Systems  Research,
December  19,  2019,  https://cisr.mit.edu/publication/MIT_CISRwp439_PepsiCoDX_Wixom
(accessed January 17, 2023).
5. Barbara H. Wixom, Killian Farrell, and Leslie Owens, “During a Crisis, Let Data Monetization
Help  Your  Bottom  Line,”  MIT  Sloan  Center  for  Information  Systems  Research,  Research
Briefing,  vol.  XX,  no.  4,  April  16,  2020,
https://cisr.mit.edu/publication/2020_0401_DataMonPortfolio_WixomFarrellOwens  (accessed
January 17, 2023).
6.  Jitendra  V.  Singh,  “Performance,  Slack,  and  Risk  Taking  in  Organizational  Decision  Making,”
The Academy of Management Journal 29, no. 3 (1986): 562–585; L. Jay Bourgeois III, “On the
Measurement of Organizational Slack,” The Academy of Management Review 6, no. 1 (1981): 29–
39.
7. Barbara H. Wixom, “Data Monetization: Generating Financial Returns from Data and Analytics
—Summary  of  Survey  Findings,”  Working  Paper  No.  437,  MIT  Sloan  Center  for  Information
Systems  Research,  April  18,  2019,
https://cisr.mit.edu/publication/MIT_CISRwp437_DataMonetizationSurveyReport_Wixom
(accessed January 17, 2023).
8. Steven Rosenbush and Laura Stevens, “At UPS, the Algorithm Is the Driver,” Wall Street Journal,
February  16,  2015,  https://www.wsj.com/articles/at-ups-the-algorithm-is-the-driver-1424136536
(accessed August 30, 2022).
9.  Clint  Boulton,  “Columbia  Sportswear  Boosts  Profit  with  Focus  on  Supply  Chain,”  Wall  Street
Journal,  May  8,  2015,  https://www.wsj.com/articles/columbia-sportswear-boosts-profit-with-
focus-on-supply-chain-1431121627 (accessed August 30, 2022).
10.  Barbara  H.  Wixom,  “Winning  with  IoT:  It’s  Time  to  Experiment,”  MIT  Sloan  Center  for
Information  Systems  Research,  Research  Briefing,  vol.  XVI,  no.  11,  November  17,  2016,
https://cisr.mit.edu/publication/2016_1101_IoT-Readiness_Wixom (accessed January 17, 2023).
11.  Thomas  H.  Davenport  and  James  E.  Short,  “The  New  Industrial  Engineering:  Information
Technology and Business Process Redesign,” Sloan Management Review (1990 Summer), 11–27;
Michael Hammer, “Reengineering Work: Don’t Automate, Obliterate!,” Harvard Business Review
(July-Aug  1990),  104–112;  Michael  Hammer  and  James  Champy,  Reengineering  the
Corporation: A  Manifesto for Business  Revolution (New  York: HarperBusiness,  1993); Thomas

H. Davenport, Process Innovation (Cambridge, MA:  Harvard Business School Press, 1993); W.
Edwards  Deming,  The  New  Economics:  For  Industry,  Government,  Education,  3rd  ed.
(Cambridge, MA: MIT Press, 2018).
12.  Greg  Geracie  and  Steven  D.  Eppinger,  eds.,  The  Guide  to  the  Product  Management  and
Marketing  Body  of  Knowledge:  ProdBOK(R)  Guide  (Carson  City,  NV:  Product  Management
Educational Institute, 2013), 31.
13. Malcom Frank, Paul Roehrig, and Ben Pring, Code Halos (Hoboken, NJ: John  Wiley & Sons,
2014).
14.  “Our  History,”  Nielsen,  https://sites.nielsen.com/timelines/our-history  (accessed  August  20,
2022);  “Our  Heritage  of  Innovation,  Transformation  and  Growth,”  IRI,
https://www.iriworldwide.com/en-us/company/history (accessed February 11, 2022).
15.  Anne  Buff,  Barbara  H.  Wixom,  and  Paul  P.  Tallon,  “Foundations  for  Data  Monetization,”
Working Paper No. 402, MIT Sloan Center for Information Systems Research, August 17, 2015,
https://cisr.mit.edu/publication/MIT_CISRwp402_FoundationsForDataMonetization_BuffWixom
Tallon (accessed January 17, 2023).
16.  “Business  and  Weather  Data:  Keys  to  Improved  Decisions,”  IBM,
https://www.ibm.com/products/weather-company-data-packages (accessed August 30, 2022).

Chapter 2
1. Barbara H. Wixom, “Data Monetization: Generating Financial Returns from Data and Analytics
—Summary  of  Survey  Findings,”  Working  Paper  No.  437,  MIT  Sloan  Center  for  Information
Systems  Research,  April  18,  2019,
https://cisr.mit.edu/publication/MIT_CISRwp437_DataMonetizationSurveyReport_Wixom
(accessed January 17, 2023).
2. Wixom, “Data Monetization.”
3. Barbara H. Wixom and Killian Farrell, “Building Data Monetization Capabilities That Pay Off,”
MIT  Sloan  Center  for  Information  Systems  Research,  Research  Briefing,  vol.  XIX,  no.  11,
November  21,  2019,
https://cisr.mit.edu/publication/2019_1101_DataMonCapsPersist_WixomFarrell  (accessed
January 17, 2023).
4.  We  initially  noted  the  connections  between  practices  and  capabilities  in  our  case  research.  We
confirmed these relationships in survey research; see Wixom, “Data Monetization.”
5. Barbara H. Wixom, Ida A. Someh, Angela Zutavern, and Cynthia M. Beath, “Explanation: A New
Enterprise Data Monetization Capability for AI,” Working Paper No. 443, MIT Sloan Center for
Information  Systems  Research,  July  1,  2020,
https://cisr.mit.edu/publication/MIT_CISRwp443_SucceedingArtificialIntelligence_WixomSome
hZutavernBeath (accessed January 17, 2023).
6. Barbara H. Wixom and Gabriele Piccoli, “Build Data Liquidity to Accelerate Data Monetization,”
MIT Sloan Center for Information Systems Research, Research Briefing, vol. XXI, no. 5, May 20,
2021, https://cisr.mit.edu/publication/2021_0501_DataLiquidity_WixomPiccoli (accessed January
17, 2023).
7. Ida A. Someh, Barbara H. Wixom, and Cynthia M. Beath, “Building AI Explanation Capability
for  the  AI-Powered  Organization,”  MIT  Sloan  Center  for  Information  Systems  Research,
Research  Briefing,  vol.  XXII,  no.  7,  July  21,  2022,
https://cisr.mit.edu/publication/2022_0701_AIX_SomehWixomBeath  (accessed  January  17,
2023).
8. Barbara H.  Wixom and  M. Lynne  Markus. “To Develop  Acceptable Data Use,  Build Company
Norms,” MIT Sloan Center for Information Systems Research, Research Briefing, vol. XVII, no.
4, April 20, 2017, https://cisr.mit.edu/publication/2017_0401_AcceptableDataUse_WixomMarkus
(accessed January 17, 2023).
9.  Barbara  H.  Wixom,  Gabriele  Piccoli,  Ina  Sebastian,  and  Cynthia  M.  Beath,  “Anthem’s  Digital
Data Sandbox,”  Working Paper  No.  451,  MIT Sloan  Center for  Information  Systems Research,
October  1,  2021,
https://cisr.mit.edu/publication/MIT_CISRwp451_Anthem_WixomPiccoliSebastianBeath
(accessed  January  17,  2023).  In  2022,  Anthem  Health  was  renamed  Elevance  Health  (see
https://www.elevancehealth.com).
10.  Elena  Alfaro,  Juan  Murillo,  Fabien  Girardin,  Barbara  H.  Wixom,  and  Ida  A.  Someh,  “BBVA
Fuels Digital Transformation Progress with a Data Science Center of Excellence,” Working Paper
No.  430,  MIT  Sloan  Center  for  Information  Systems  Research,  April  27,  2018,
https://cisr.mit.edu/publication/MIT_CISRwp430_BBVADataScienceCoE_AlfaroMurilloGirardin
WixomSomeh (accessed  January  17,  2023).  The  paper  was  the  winner  of  the  2018  Best  Paper
Competition  from  the  Society  for  Information  Management;  “BBVA,  an  Overall  Digital
Experience  Leader  Five  Years  in  a  Row,  According  to  ‘European  Mobile  Banking  Apps,  Q3
2021,’” Banco Bilbao Vizcaya Argentaria, February 11, 2022, https://www.bbva.com/en/bbva-an-

overall-digital-experience-leader-five-year-in-a-row-according-to-european-mobile-banking-apps-

q3-2021 (accessed August 30, 2022).

11. Wixom and Farrell, “Building Data Monetization Capabilities That Pay Off.”

Chapter 3
1. Barbara H. Wixom, “Data Monetization: Generating Financial Returns from Data and Analytics
—Summary  of  Survey  Findings,”  Working  Paper  No.  437,  MIT  Sloan  Center  for  Information
Systems  Research,  April  18,  2019,
https://cisr.mit.edu/publication/MIT_CISRwp437_DataMonetizationSurveyReport_Wixom
(accessed January 17, 2023).
2. Barbara H. Wixom, Ida A. Someh, Angela Zutavern, and Cynthia M. Beath, “Explanation: A New
Enterprise Data Monetization Capability for AI,” Working Paper No. 443, MIT Sloan Center for
Information  Systems  Research,  July  1,  2020,
https://cisr.mit.edu/publication/MIT_CISRwp443_SucceedingArtificialIntelligence_WixomSome
hZutavernBeath (accessed January 17, 2023).
3. Barbara H. Wixom, Ida A. Someh, and Robert W. Gregory, “AI Alignment: A New Management
Paradigm,” MIT Sloan Center for Information Systems Research, Research Briefing, vol. XX, no.
11,  November  19,  2020,  https://cisr.mit.edu/publication/2020_1101_AI-
Alignment_WixomSomehGregory (accessed January 17, 2023).
4.  Barbara  H.  Wixom  and  Jeanne  W.  Ross,  “The  U.S.  Securities  and  Exchange  Commission:
Working  Smarter  to  Protect  Investors  and  Ensure  Efficient  Markets,”  Working  Paper  No.  388,
MIT  Sloan  Center  for  Information  Systems  Research,  November  30,  2012,
https://cisr.mit.edu/publication/MIT_CISRwp388_SEC_WixomRoss (accessed January 17, 2023).
5.  Barbara  H.  Wixom  and  Anne  Quaadgras,  “GUESS?,  Inc.:  Engaging  the  Business  Community
with  the  “New  Look”  of  Business  Intelligence,”  MIT  Sloan  Center  for  Information  Systems
Research,  Research  Briefing,  vol.  XIII,  no.  8,  August  15,  2013,
https://cisr.mit.edu/publication/2013_0801_GUESS_WixomQuaadgras  (accessed  January  17,
2023).
6.  Barbara  H.  Wixom,  “Winning  with  IoT:  It’s  Time  to  Experiment,”  MIT  Sloan  Center  for
Information  Systems  Research,  Research  Briefing,  vol.  XVI,  no.  11,  November  17,  2016,
https://cisr.mit.edu/publication/2016_1101_IoT-Readiness_Wixom (accessed January 17, 2023).
7.  Nitan  Nohria  and  Ranjay  Gulati,  “Is  Slack  Good  or  Bad  for  Innovation?”  Academy  of
Management  Journal  39,  no.  5  (1996):  1245–1264;  Joseph  L.C.  Cheng  and  Idalene  F.  Kesner,
“Organizational Slack and Response to Environmental Shifts: The Impact of Resource Allocation
Patterns,” Journal of Management 23, no. 1 (1997): 1–18.
8.  “Market  Capitalization  of  Microsoft  (MSFT)  June  2022,”  Companies  Market  Cap,
https://companiesmarketcap.com/microsoft/marketcap (accessed June 2022).
9. Wixom, “Data Monetization.”
10. Barbara H. Wixom and Killian Farrell, “Building Data Monetization Capabilities That Pay Off,”
MIT  Sloan  Center  for  Information  Systems  Research,  Research  Briefing,  vol.  XIX,  no.  11,
November  21,  2019,
https://cisr.mit.edu/publication/2019_1101_DataMonCapsPersist_WixomFarrell  (accessed
January 17, 2023).

Chapter 4
1.  Ronny  Schüritz,  Killian  Farrell,  and  Barbara  H.  Wixom,  “Creating  Competitive  Products  with
Analytics—Summary  of  Survey  Findings,”  Working  Paper,  No.  438,  MIT  Sloan  Center  for
Information  Systems  Research,  June  28,  2019,
https://cisr.mit.edu/publication/MIT_CISRwp438_DataWrappingParticipantReport_SchuritzFarre
llWixom (accessed January 17, 2023).
2. Barbara H. Wixom and Ronny Schüritz, “Creating Customer Value Using Analytics,” MIT Sloan
Center for  Information  Systems  Research,  Research Briefing,  vol.  XVII,  no.  11, November  16,
2017,  https://cisr.mit.edu/publication/2017_1101_WrappingAtCochlear_WixomSchuritz
(accessed January 17, 2023).
3. Wixom and Schüritz, “Creating Customer Value Using Analytics.”
4. Wixom and Schüritz, “Creating Customer Value Using Analytics.”
5. Ronny Schüritz, Killian Farrell, Barbara H. Wixom, and Gerhard Satzger, “Value Co-Creation in
Data-Driven  Services:  Towards  a  Deeper  Understanding  of  the  Joint  Sphere,”  International
Conference  for  Information  Systems,  December  15–18,  2019;  Christian  Grönroos  and  Päivi
Voima, “Critical Service Logic: Making Sense of Value Creation and Co-Creation,” Journal of the
Academy of Marketing Science 41, no. 2 (2013): 133–150.
6. Barbara H. Wixom and Ina M. Sebastian, “Don’t Leave Value to Chance: Build Partnerships with
Customers,” MIT Sloan Center for Information Systems Research, Research Briefing, vol. XIX,
no.  12,  December  19,  2019,
https://cisr.mit.edu/publication/2019_1201_PepsiCoCustomerPartnerships_WixomSebastian
(accessed January 17, 2023).
7. “PepsiCo Annual Report, 2021,”  PepsiCo, https://www.pepsico.com/docs/default-source/annual-
reports/2021-annual-report.pdf (accessed August 30, 2022).
8. Barbara H. Wixom, “PepsiCo Unlocks Granular Growth Using a Data-Driven Understanding of
Shoppers,”  Working  Paper  No.  439,  MIT  Sloan  Center  for  Information  Systems  Research,
December  19,  2019,  https://cisr.mit.edu/publication/MIT_CISRwp439_PepsiCoDX_Wixom
(accessed January 17, 2023).
9.  Margaret  A.  Neale,  and  Thomas  Z.  Lys,  Getting  (More  of)  What  You  Want  (London:  Profile
Books, 2015).
10.  Dale  Goodhue  and  Barbara  H.  Wixom,  “Carlson  Hospitality  Worldwide  KAREs  about  Its
Customers,” in Harnessing Customer Information for Strategic Advantage: Technical Challenges
and Business Solutions, ed. W. Eckerson and H. Watson (Seattle: The Data Warehousing Institute,
2000).
11. Barbara H. Wixom, “Data Monetization: Generating Financial Returns from Data and Analytics
—Summary  of  Survey  Findings,”  Working  Paper  No.  437,  MIT  Sloan  Center  for  Information
Systems  Research,  April  18,  2019,
https://cisr.mit.edu/publication/MIT_CISRwp437_DataMonetizationSurveyReport_Wixom
(accessed January 17, 2023).
12. Barbara  H.  Wixom and  Ronny  Schüritz, “Making  Money  from Data  Wrapping: Insights  from
Product Managers,” MIT Sloan Center for Information Systems Research, Research Briefing, vol.
XVIII,  no.  12,  December  20,  2018,
https://cisr.mit.edu/publication/2018_1201_WrappingValue_WixomSchuritz  (accessed  January
17, 2023).
13. Barbara H. Wixom and Killian Farrell, “Building Data Monetization Capabilities That Pay Off,”
MIT  Sloan  Center  for  Information  Systems  Research,  Research  Briefing,  vol.  XIX,  no.  11,

November 21, 2019,

https://cisr.mit.edu/publication/2019_1101_DataMonCapsPersist_WixomFarrell (accessed

January 17, 2023).

14. Wixom, “Data Monetization.”

Chapter 5
1.  Anne  Buff,  Barbara  H.  Wixom,  and  Paul  P.  Tallon,  “Foundations  for  Data  Monetization,”
Working Paper No. 402, MIT Sloan Center for Information Systems Research, August 17, 2015,
https://cisr.mit.edu/publication/MIT_CISRwp402_FoundationsForDataMonetization_BuffWixom
Tallon (accessed January 17, 2023).
2. “8451: Who We Are,” 8451, https://www.8451.com/who-we-are (accessed August 30, 2022).
3. Barbara H. Wixom, “Data Monetization: Generating Financial Returns from Data and Analytics
—Summary  of  Survey  Findings,”  Working  Paper  No.  437,  MIT  Sloan  Center  for  Information
Systems  Research,  April  18,  2019,
https://cisr.mit.edu/publication/MIT_CISRwp437_DataMonetizationSurveyReport_Wixom
(accessed January 17, 2023).
4. Barbara H. Wixom and Jeanne W. Ross, “Profiting from the Data Deluge,” MIT Sloan Center for
Information  Systems  Research,  Research  Briefing,  vol.  XV,  no.  12,  December  17,  2015,
https://cisr.mit.edu/publication/2015_1201_DataDeluge_WixomRoss  (accessed  January  10,
2023).
5. Buff, Wixom, and Tallon, “Foundations for Data Monetization.”
6. “Global Data Broker Market Size, Share, Opportunities, COVID-19 Impact, and Trends by Data
Type  (Consumer  Data,  Business  Data),  by  End-User  Industry  (BFSI,  Retail,  Automotive,
Construction, Others), and by Geography—Forecasts from 2021 to 2026,” Knowledge Sourcing
Intelligence,  June  2021,  https://www.knowledge-sourcing.com/report/global-data-broker-market
(accessed August 30, 2022).
7. “About Verisk,” Verisk, https://www.verisk.com/about (accessed August 30, 2022); “Verisk Fact
Sheet,”  Verisk  Inc.  Newsroom,  https://www.verisk.com/newsroom/verisk-fact-sheet  (accessed
August 30, 2022).
8.  Jennifer  Belissent,  “The  Insights  Professional’s  Guide  to  External  Data  Sourcing,”  Forrester
Research,  Inc.,  August  2,  2021,  https://www.forrester.com/report/The-Insights-Professionals-
Guide-To-External-Data-Sourcing/RES139331 (accessible behind paywall August 30, 2022).
9.  Gabriele  Piccoli,  Federico  Pigni,  Joaquin  Rodriguez,  and  Barbara  H.  Wixom,  “TRIPBAM:
Creating Digital Value at the Time of the COVID-19 Pandemic,” Working Paper No. 444, MIT
Sloan  Center  for  Information  Systems  Research,  July  30,  2020,
https://cisr.mit.edu/publication/MIT_CISRwp444_TRIPBAM_PiccoliPigniRodriguezWixom
(accessed January 10, 2023).
10.  Barbara  H.  Wixom,  Cynthia  M.  Beath,  Ja-Nae  Duane,  and  Ida  A.  Someh,  “Healthcare  IQ:
Sensing and Responding to Change,” Working Paper No. 458, MIT Sloan Center for Information
Systems  Research,  February  1,  2023,
https://cisr.mit.edu/publication/MIT_CISRwp458_HealthcareIQDataAssets_WixomBeathDuaneS
omeh  (accessed  February  17,  2023);  Barbara  H.  Wixom  and  Cheryl  Miller,  “Healthcare  IQ:
Competing as the ‘Switzerland’ of Health Spend Analytics,” Working Paper No. 400, MIT Sloan
Center  for  Information  Systems  Research,  February  6,  2015,
https://cisr.mit.edu/publication/MIT_CISRwp400_HealthcareIQ_WixomMiller  (accessed
February 17, 2023).
11.  Jay  B.  Barney,  “Looking  Inside  for  Competitive  Advantage,”  The  Academy  of  Management
Executive (1993–2005) 9, no. 4 (1995): 49–61.
12.  Magid Abraham,  “Data  Monetization  Strategies  That  Can Show  You  the  Money,”  MIT  Sloan
Center for Information Research, MIT CISR Summer Session, June 18, 2014.

13. Barbara H. Wixom, Anne Buff, and Paul P. Tallon, “Six Sources of Value for Information

Businesses,” MIT Sloan Center for Information Systems Research, Research Briefing, vol. XV,

no. 1, January 15, 2015,

https://cisr.mit.edu/publication/2015_0101_DataMonetizationValue_WixomBuffTallon (accessed

January 10, 2023).

14. Barbara H. Wixom and Killian Farrell, “Building Data Monetization Capabilities That Pay Off,”

MIT Sloan Center for Information Systems Research, Research Briefing, vol. XIX, no. 11,

November 21, 2019,

https://cisr.mit.edu/publication/2019_1101_DataMonCapsPersist_WixomFarrell (accessed

January 17, 2023).

15. Barbara H. Wixom and M. Lynne Markus, “To Develop Acceptable Data Use, Build Company

Norms,” MIT Sloan Center for Information Systems Research, Research Briefing, vol. XVII, no.

4, April 20, 2017, https://cisr.mit.edu/publication/2017_0401_AcceptableDataUse_WixomMarkus

(accessed January 10, 2023); Dorothy Leidner, Olgerta Tona, Barbara H. Wixom, and Ida A.

Someh, “Make Dignity Core to Employee Data Use,” Sloan Management Review, September 22,

2021. Reprint #63215.

Chapter 6

1. Ida Someh, Barbara H. Wixom, Michael J Davern, and Graeme Shanks, “Configuring

Relationships Between Analytics and Business-Domain Groups for Knowledge Integration,” JAIS

Preprints (forthcoming), http://aisel.aisnet.org/jais_preprints/63 (accessed January 17, 2023).

2. Someh, Wixom, Davern, and Shanks, “Configuring Relationships.”

3. This thought experiment is inspired by events at a conference that Barbara Wixom attended years

ago hosted by TDWI (Transforming Data with Intelligence), an education and research provider.

Data leaders invited to the conference were encouraged to bring an executive business champion.

At the start of the conference, the data leaders received a red shirt and the business champions

received a blue one. At the end of the conference, everyone left with a purple shirt.

4. Ida A. Someh and Barbara H. Wixom, “Microsoft Turns to Data to Drive Business Success,”

Working Paper No. 419, MIT Sloan Center for Information Systems Research, July 28, 2017,

https://cisr.mit.edu/publication/MIT_CISRwp419_MicrosoftDataServices_SomehWixom

(accessed January 17, 2023).

5. Ida A. Someh and Barbara H. Wixom, “Data-Driven Transformation at Microsoft,” MIT Sloan

Center for Information Systems Research, Research Briefing, vol. XVII, no. 8, August 17, 2017,

https://cisr.mit.edu/publication/2017_0801_DataDrivenTransformation_SomehWixom (accessed

January 17, 2023).

Chapter 7
1.  Donald  C.  Hambrick  and  James  W.  Frederickson,  “Are  You  Sure  You  Have  a  Strategy?”  The
Academy of Management Executive 19, no. 4 (2001): 48–59.
2. Wayne Eckerson, The Data Strategy Guidebook: What Every Executive Needs to Know (Boston,
MA: Eckerson Group, 2019).
3. Barbara H. Wixom, Killian Farrell, and Leslie Owens, “During a Crisis, Let Data Monetization
Help  Your  Bottom  Line,”  MIT  Sloan  Center  for  Information  Systems  Research,  Research
Briefing,  vol.  XX,  no.  4,  April  16,  2020,
https://cisr.mit.edu/publication/2020_0401_DataMonPortfolio_WixomFarrellOwens  (accessed
January 17, 2023).
4.  Veerai  Desai,  Tim  Fountaine,  and  Kayvaun  Rowshankish,  “A  Better  Way  to  Put  Your  Data  to
Work,” Harvard Business Review (July-Aug 2022), 3–9.
5. Stephanie L. Woerner, Peter Weill, and Ina M. Sebastian, Future Ready: The Four Pathways to
Capturing Digital Value (Cambridge, MA: Harvard Business Review Press, 2022).
Appendix
1. Ida A. Someh, Barara H. Wixom, and Cynthia M. Beath, “Building AI Explanation Capability for
the  AI-Powered  Organization,” MIT  Sloan  Center  for  Information  Systems,  Research  Briefing,
vol.  XXII,  no.  7,  July  21,  2022,
https://cisr.mit.edu/publication/2022_0701_AIX_SomehWixomBeath  (accessed  January  17,
2023).
2. Barbara H. Wixom, “Data Monetization: Generating Financial Returns from Data and Analytics
—Summary  of  Survey  Findings,”  Working  Paper  No.  437,  MIT  Sloan  Center  for  Information
Systems  Research,  April  18,  2019,
https://cisr.mit.edu/publication/MIT_CISRwp437_DataMonetizationSurveyReport_Wixom
(accessed January 17, 2023).

Index
Abraham, Magid, 103
A/B testing, 34, 44
Acceptable data use, 3, 29, 35–36, 59, 63, 86, 106, 108
Action improvements, 49, 52–53
Action solutions, 94, 97–98, 99–100, 101
Action wraps, 69–71, 72–73, 75, 76, 89
Advisory services connection, 115, 117, 120
Aggregators, 7, 20
AI, 33, 49, 51, 52–53, 55
Alphabet, Inc., 1
Ambiata, 34–35
Analytics tools, 33
Anthem Health, 35
Application programming interfaces (APIs), 21, 32, 86
Archetypes, data monetization strategy, 132–136
Automation, 35, 52, 97–98
Banco Bilbao Vizcaya Argentaria (BBVA). See BBVA (Banco Bilbao Vizcaya Argentaria)
Bank card data, anonymized, 40–42, 93
Bartering, 20
BBVA (Banco Bilbao Vizcaya Argentaria), 10, 40–44, 73–75, 93, 141, 151–152
BBVA Data & Analytics (D&A), 41–44, 69, 93, 151–152, 153
Bloomberg, 21
Bottom lines, 10–11, 13, 24, 81, 82
Business intelligence reporting, 49
Business Process Reengineering (BPR) movement, 17
Business strategy, 131
Business-to-business (B2B) settings, 69, 78
Business-to-consumer (B2C) settings, 69
Capabilities, data monetization, 3–4, 7–8, 25–45, 59–61, 83–87, 148
acceptable data use, 3, 29, 35–36, 59, 63, 86, 106, 108
assessment of, 36
customer understanding, 3, 29–30, 33–35, 38, 59, 61, 62–63, 87, 108
data management, 3, 27, 31–32, 59, 61–62, 86, 107
data platform, 3, 27, 32–33, 59, 62, 86, 107–108

data science, 3, 29, 31, 33, 59, 62, 86, 108
Carlson Hospitality, 82
CarMax, 9–10, 129
Chatbots, 71–72
Chief data office, 40
Cloud-based services, 57, 58
Cloud-native technology, 32, 38, 41, 86
Cocreation, 34
Collaboration, 41, 57, 79, 100, 125
Colours IQ, 99, 108
Columbia Sportswear, 17
Competitive pressure, 20, 23, 102–103
Competitive Strength Index, 133, 135, 137, 139, 141
Comscore, 103
Contractor assessments, 49, 53
Cook, Sean, 147
Copycat providers, 103
Credit scores, 96–97
Curated data, 32
Customer churn, 118
Customer focus strategy, 132, 136–138
Customer journey mapping initiatives, 9, 29–30
Customer Labs, 34
Customer retention, 20
Customer satisfaction, 22
Customer understanding, 3, 29–30, 33–35, 38, 59, 61, 62–63, 87, 108
Data access, 59, 63
Data access approval processes, 35
Data assets
accumulation of, 42–43
augmenting, 32
description of, 2
evaluation of, 149–150
external access to, 32
information solutions and, 95
new ways of working and, 10
reusing, 25, 31, 45, 63
safeguards for, 106
Data broker market, 95
Data democracies, 113–127, 145, 150
Data democracy incentives, 114, 122–126

Data-domain connections, 114–122
Data experts, 113–114
Data improvements, 49–51
Data-insight-action process, 7, 12, 22
Data management, 3, 27, 31–32, 59, 61–62, 86, 107
Data Management Association, 3
Data monetization
approaches to, 8, 16–23
capabilities for improving, 59–61
capabilities for selling, 104–106
capabilities for wrapping, 83–85
current state evaluation of, 148–151
definition of, 10
frameworks for, 3–6
percentage of in overall revenues, 12
strategy, 130–131
Data Monetization Capability Index, 130–131, 133, 136, 137, 139, 141
Data ownership, 35
Data platform, 3, 27, 32–33, 59, 62, 86, 104, 107–108
Data race, 38–40
Data savvy, 3, 114
Data science, 3, 29, 31, 33, 59, 62, 86, 108
Data solutions, 93, 95–96, 99, 101
Data sources and flows, 31–32
Data storytelling, 33
Data strategy, 131
Data wraps, 69–71, 75, 76, 89
Decontextualizing data, 2, 3–4
Definitions, standard, 31
Demand Accelerator, 79–80, 84–87, 88, 138, 153
Design thinking, 9
Diffusion connections, 119–120
Digital strategy, 131
Direction, setting, 130–132
Domain expertise, 5, 93, 104, 113–114, 115, 117
DuBois, Jim, 61
Efficiencies, 15–16, 18, 55, 80–82
Embedded experts, 115, 117–118, 121, 138
Enhancements, 18–20
Enterprise capabilities, 26, 36, 38–40, 45, 48, 61
Equipment failure predictions, 51–52

Evaluation of current state, 148–151
Evidence-based decision-making, 33, 57
Experimentation, 34
Experts/expertise, 3–6, 26, 93, 97, 104, 113–114, 138
External access, 32
External oversight, 35
eatures and experiences, 4–5
idelity Investments, 32–33
inancial analysts, 57–59, 64
orrester, award from, 10
our As, 73–75, 83
ractal maps, 103
raud reduction wraps, 82
uture-ready strategy, 132, 140–142
Gainsharing model, 101
GE, 49, 53, 55
General Data Protection Regulation (GDPR) requirements, 122
Goals, 47, 57, 65
Google, 1, 2, 57, 141
GUESS, 51, 55, 64
Handwashing, 54
Healthcare IQ, 98–100, 101, 102–103, 106–109, 139, 140, 153
HITRUST certification, 108
Hootman, Brandon, 25
Hypothesis testing, 34
AG, 34, 38
BM’s Weather Company, 21
mage processing, 33
mprove-wrap-sell framework, 21–23
mproving/improvements
capabilities for, 59–63
case study on, 42–43, 56–59, 61–63
description of, 16–18
frameworks for, 4–5
improve-wrap-sell framework and, 21–23, 24
introduction to, 47–48
ownership and, 63–64
reflection on, 64–65
types of, 49, 49–53

value creation and, 53–55
value realization and, 55–56
nformation business strategy, 132, 138–140
nformation solutions
action solutions, 94, 97–98, 99–100, 101
case study on, 98–100, 106–109
data solutions, 93, 95–96, 99, 101
nformation solutions (cont.)
description of, 20–21
insight solutions, 94, 96–97, 99, 101
selling, 4–5, 91–111
types of, 93–100
nitiatives, 4–5, 8
nnovation connections, 117–118
nsight improvements, 49, 51–52
nsight solutions, 94, 96–97, 99, 101
nsight wraps, 69–72, 75, 79, 89
ntegrated data, 31–32
nternal access, 32, 59
nternal oversight, 35
RI, 20, 27
ankowski, Gregg, 67
oint sphere, 76–78
Kroger, 92, 93
ab images, abnormalities in, 52
amond, David, 129
ocal capabilities, 26, 40
oyalty programs, 82
Machine learning, 31, 33
Madoff, Bernie, 50
Master data, 59
Measurement approaches, 53
Metadata, 31
Microsoft, 7, 10, 56–59, 61–64, 65, 120–122, 123, 125–126, 136
Microsoft Azure cloud technology, 62
Microsoft Sales Experience platform, 62, 126
MIT Center for Information Systems Research (MIT CISR), 6–7, 48, 142, 153
MIT Senseable City Lab, 40–42
Monetization, use of term, 23

Most Valuable Shopper data, 86
Multidisciplinary teams, 115, 117–118, 121, 137
Nadella, Satya, 57, 58, 59, 61, 65, 121, 123, 125–126
Natural language processing, 33
Next-best offers, 118
Nielsen, 15, 27
OM Solutions, 91–92
Ontology, 32
Open data websites, 94
Operational optimization strategy, 132, 133, 135–136
Organizational design, 5, 115, 121, 123
Owens and Minor (OM), 91, 93
Ownership
improving and, 63–64
selling and, 109–110
wrapping and, 87–88
ampers, 19
assenger demand forecasts, 52
epsiCo, 10, 78–80, 84–87, 88, 137–138, 153
hillips, Robert, 47
ilot tests, 53–54
oint-of-sale (POS) transaction data, 20, 69, 92
ower, incentives and, 123–125
ower BI, 57, 62
ractices, data monetization capabilities and, 29–36, 45
ricing strategies, 101
rivacy, 147
rocess owners, 63–64, 65
roduct enhancements, 15
roduct sales, 56
rogress, tracking, 151–153
ublic-sector initiatives, 94
urple people, 115, 117, 118, 139, 148, 150
Radisson, 82
Rebba, Jeevan, 9
Reporting, 33
Reputational risk, 88
Return on investment (ROI) from wraps, 82, 100, 147–148
Risks

improve-wrap-sell framework and, 22–23
reputational, 88
ales processes, 61–63
amuel, Rob, 113
chindler, 19
EC (US Securities and Exchange Commission), 50–51, 54
elling
capability considerations for, 104–109
case study on, 40–42
description of, 16, 20–21
improve-wrap-sell framework and, 21–23, 24
information solutions and, 91–111
ownership and, 109–110
reflection on, 110–111
value creation and, 100
value realization and, 11, 15, 101–104
ensemaking, 34, 62, 84
hah, Mihir, 1
hared services, 115, 117, 119, 122
hared value agreements, 101
lack, 15–16, 18, 55, 56, 64, 80
ocial networks, 115, 117, 119, 122
ocial norms, incentives and, 125
omeh, Ida A., 115
pecialization, 26
pend categorizers, 43–44, 69, 73–75
tatistics, 33
toller, Don, 91
torytelling training program, 58
trategy and vision, 8, 129–146, 148
witching costs, 20
Taxonomy, 32
TCR (tips, complaints, and referrals) database, 50, 54
Test and learn processes, 9
Training, 35, 51, 58
Trinity Health, 17, 52, 53–54, 64
TRIPBAM, 95–98, 100, 101, 140
Trust, 78, 79, 97–98
United Parcel Service (UPS), 17
Uplift, 76

US Securities and Exchange Commission (SEC), 50–51, 54
Value creation
description of, 11, 12
improving and, 47–48, 49, 53–55
reflection on, 23–24
risk and, 22
selling and, 100
value realization and, 15
wrapping and, 76–80
Value-effort matrix, 143–145
Value proposition, incentives and, 125–126
Value realization
description of, 11, 13–15
improve-wrap-sell framework and, 16, 24
improving and, 55–56, 59
selling and, 101–104
wrapping and, 80–83
Value Realization Index, 133, 135, 137, 139, 140
Verisk, Inc., 95
World Bank, 19
Wraps/wrapping
action wraps, 69–71, 72–73, 76, 89
capabilities for, 83–87
case study on, 43–44, 78–80, 84–87
characteristics of great, 73–75
data assets, 75
data wraps, 69–71, 75, 76, 89
description of, 16, 18–20
examples of, 67–68
improve-wrap-sell framework and, 21–23, 24
initiatives using, 87–88
insight wraps, 69–72, 75, 79, 89
joint sphere and, 76–78
measuring value from, 81–83
ownership of, 87–88
reflection on, 88–89
ROI from, 82, 100, 147–148
types of, 68–73
value creation and, 76–80
value realization and, 80–83